text:
\section{Introduction}
\label{sec:intro}
Human behaviour analysis through skeleton-based data has been widely investigated for decades. The advent of deep learning-based architectures increased its popularity even more, mainly due to the robustness of skeleton data in handling dynamic circumstances, appearance variations, and cluttered backgrounds. Over the last decade, the rise of data-driven approaches highly correlates performance with the scale of the learning set. Hence, generating high-quality synthetic human actions can address the problem of limited data. However, existing methods are still severely limited, particularly in conditioning desirable actions and considering the generation at the global movement level. \par

\begin{figure}[t]
  \begin{center}
  \includegraphics[width=8.4cm]{images/blender/intro.png}
  \caption{\textbf{Synthetic set of actions} generated by our graph convolutional generator trained on NTU RGB+D \cite{shahroudy2016ntu} (first two rows) and NTU-120 RGB+D \cite{liu2019ntu} (last row). Kinetic-GAN is able to generate up to 120 different actions even under global movement settings. See accompanying video.}
  \label{fig:intro}
  \end{center}
  \vspace{-2em}
\end{figure}

\begin{figure*}[h]
  \begin{center}
  \includegraphics[width=\textwidth]{images/overview_v4.pdf}
  \caption{\textbf{Cohesive view of the proposed Kinetic-GAN}. The blue and red components belong to the generator and discriminator, respectively. First, both the Gaussian random noise $\bm{z}$ and the embedded action class representation $\bm{y}$ are concatenated and mapped to an intermediate latent space $\mathbfcal{W}$, which is fed to the generator and upsampled spatially and temporally through each resolution level $l$. The discriminator receives a skeleton graph sequence $\mathbfcal{G}_l$ together with its embedded class (channel-wise concatenation) and learns to discriminate by downsampling spatially (coarsening) and temporally from level $l$ to 0.}
  \label{fig:gan_overview}
  \end{center}
  \vspace{-1.8em}
\end{figure*}

The existing skeleton-based human action synthesis algorithms are classified into two categories: autoregressive and generative approaches. Autoregressive approaches \cite{fragkiadaki2015recurrent, kundu2019unsupervised, zhou2018auto} are usually based on Recurrent Neural Networks (RNNs) and consider the skeleton data as a vector sequence to model an action from several seen frames. Despite the decent quality of the individual samples, autoregressive approaches have two disadvantages: (1) Extracting inherent structural body information using vectorized skeleton sequences is suboptimal. (2) The use of LSTMs \cite{hochreiter1997long}, GRU \cite{bahdanau2014neural}, and Seq2Seq \cite{sutskever2014sequence} potentially limits the scalability regarding the \textit{bidirectional temporal dependency}, i.e., a future frame modifying the past ones, and the increased difficulty of a past frame conditioning a distant future frame. To solve the latter drawback, generative approaches such as \cite{barsoum2018hp, cai2018deep, yan2019convolutional, yu2020structure} use the concept of Generative Adversarial Networks \cite{goodfellow2014generative} (GANs) to produce an entire body skeleton sequence from a latent space. However, this category also has some shortcomings: (1) most approaches still employ manually structured vector sequences to model skeleton data, and (2) they rely on autoregressive techniques and Gaussian processes to solve long-term relationships over the latent space, which greatly limits their scalability in action conditioning.\par

This paper proposes a Generative Adversarial Graph Convolutional Network (Kinetic-GAN) to address the above-mentioned limitations. Our architecture leverages the benefits of GANs and Graph Convolutional Networks (GCNs), such that we generate conditioned human action sequences directly from the latent space while maintaining the long-term relationships between frames. \par
Inspired by the generalization of convolutions from images to graphs, we use spatiotemporal graph convolutions in the generator and discriminator to model skeleton data and exploit the skeleton's inherent graph structure, rather than manually structuring them as coordinate vector sequences. Also, the proposed approach improves the state-of-the-art in action conditioning, controlling up to 120 different actions (examples in Fig. \ref{fig:intro}), whereas previous methods could only control around 10 actions \cite{ habibie2017recurrent, wang2020learning, yu2020structure}. Additionally, the generation of human actions directly from the latent space may inspire some future directions in human behaviour analysis, such as interpretable latent space directions \cite{shen2020interpreting,voynov2020unsupervised} or style transfer between samples \cite{huang2017arbitrary, karras2019style, karras2020analyzing}. Fig. \ref{fig:gan_overview} illustrates the overview of the proposed framework.\par
In summary, our main contributions are three-fold: 1) A new scalable Generative Adversarial Graph Convolutional Network architecture to synthesise human actions. 2) An architecture that can be extended to a conditional model, generating desirable actions up to 120 different classes. 3) We perform extensive experiments on three datasets, NTU RGB+D \cite{shahroudy2016ntu}, NTU-120 RGB+D  \cite{liu2019ntu} and Human$3.6$M \cite{ionescu2013human3}, where Kinetic-GAN exceeds the state-of-the-art performance by a significant margin.

\section{Related Work}
\label{sec:rw}
Human action synthesis regards the generation of understandable spatial and temporal kinematics of the human body skeleton. Current methods extract structural and dynamic patterns from either manually structured sequences or graph-based structures. Those representations are then used to synthesise actions via either autoregressive (to learn temporal dependencies) or generative models (to learn a probability distribution).

\subsection{Skeleton-based Behaviour Analysis}
\label{ssec:rw_skl_intro}
Body pose estimation is one of the auspicious cues in human behaviour analysis. This semantically rich and very descriptive representation of human dynamics attenuates appearance noises that RGB and depth data contain, driving the learning process solely over human behaviour.\par
Over the last decade, skeleton-based behaviour analysis has evolved from pseudo-images with CNNs \cite{ke2017new, li2018co, liu2017enhanced, soo2017interpretable} and sequence coordinate vectors with RNNs \cite{du2015hierarchical, liu2016spatio, liu2017global, song2017end, zhang2017view}, to the solid improvements of GCNs \cite{chen2021multi, cheng2020skeleton, shi2019two, yan2018spatial, zhang2020context, zhang2020semantics} which models skeleton data as a spatiotemporal graph, which better represents the embedded structural information. Still, most current methods \cite{ wang2020learning, yu2020structure} employ manually structured sequence coordinate vectors.

\subsection{Autoregressive Models}
\label{ssec:rw_reg}
Inspired by action prediction models, some works \cite{fragkiadaki2015recurrent, zhou2018auto} employ autoregressive algorithms to generate human actions from several seen frames. Fragkiadaki \etal~\cite{fragkiadaki2015recurrent} proposed to incorporate an encoder-decoder network pre- and post-LSTM-units, capturing the temporal dependencies directly from the low-dimensional representation of the input skeleton. Zhou \etal~\cite{zhou2018auto} presented a conditioned LSTM network, in which the generated data were conditioned at regular intervals of the sequence.

\subsection{Generative Models}
\label{ssec:rw_gen}
Generative adversarial networks \cite{goodfellow2014generative} inspired Generative-based human action synthesis methods \cite{kundu2019unsupervised, wang2020learning, yan2019convolutional, yu2020structure}, whereas some approaches are yet attached to autoregressive techniques \cite{kundu2019unsupervised, wang2020learning, yu2020structure} or Gaussian processes \cite{yan2019convolutional}, which limits their temporal flexibility, stochastic variation, and action conditioning. Kundu \etal~\cite{kundu2019unsupervised} proposed a hierarchical feature fusion based on an RNN auto-encoder architecture with a manually structured tree of limb connections.  Yu \etal~\cite{yu2020structure} suggested using a graph convolutional network on top of an RNN to analyze the latent temporal dependencies. However, this architecture has a limitation regarding temporal length (50 frames) and action conditioning (10 actions). Yan \etal~\cite{yan2019convolutional} proposed a Gaussian process prior to the generator to analyze the dimensions of the latent space one by one (1024 dimensions). Despite reporting temporal long-term latent relations, its heavy computation on latent points correlation limits their diversity and action conditioning. This paper proposes a novel approach that generates long-term human actions without handcrafted procedures (sequence coordinate vectors or Gaussian process priors), enabling us to synthesise far more different actions with significantly more quality even with higher temporal lengths.

\section{Proposed Method}
\label{sec:proposed}
Kinetic-GAN aims to improve the generation quality of controllable action samples while increasing intra-action diversity through stochastic variation and latent space disentanglement. This section presents the proposed approach by initially introducing the GCN adopted, followed by network description, and the discussion of the improvements caried out in the generator, discriminator, and adversarial loss.

\subsection{Graph Convolutional Networks Preliminaries}
Similar to image modelling with GANs, the proposed solution employs a generator and discriminator to perform upsampling and downsampling on samples, respectively. However, since we are modelling skeleton human actions, those operations can not be treated as conventional images with CNNs, which would lead to spatial and temporal distortions. Specifically, the conventional convolutional kernels will lose some structural information embedded in the skeleton data since adjacent joints in the pseudo-image are considered as connected joints. Hence, in each skeleton graph's resolution level $l$, the Kinetic-GAN employs graph convolutions to circumvent this issue due to their ability to exploit the skeleton's inherent graph structure.\par

\begin{figure}[t]
  \begin{center}
  \includegraphics[width=8.4cm]{images/st-gcn_hybrid.pdf}
  \caption{\textbf{Spatiotemporal graph convolution used in Kinetic-GAN}. The spatial graph convolution takes as input a skeleton graph $\mathcal{G}_l$ where its corresponding adjacency matrix $\textbf{A}_l$ handles the intra-frame (spatial) convolution (red dotted line) through the respective root node (red joint) neighbourhood. The temporal (inter-frame) convolution consists of a 1-dimensional convolution performed on the same positional joints across consecutive frames.}
  \label{fig:st-gcn}
  \end{center}
  \vspace{-1.8em}
\end{figure}

In graph convolutional networks (GCNs), a spatiotemporal graph $\mathbfcal{G}_l = (\mathbfcal{V}_l, \mathbfcal{E}_l)$ represents the skeleton data with $N_l$ joints and $T_l$ frames, where $l=\{1, ..., L\}$,  and $L$ is the number of levels of the skeleton graph resolution. Therefore, the feature map of the skeleton sequence is represented as $\textbf{X}_l \in \mathbb{R}^{N_l \times T_l \times C}$, where $C$ is the number of channels, representing the joints coordinates at resolution level $L$. A GCN consists of both spatial and temporal graph convolutions. Typically, in the spatial dimension, an adjacency matrix $\textbf{A}_l \in \{0,1\}^{N_l \times N_l}$ and the corresponding identity matrix $\textbf{I}_l$ define the intra-body joints connections, which are used to regulate the receptive fields of the convolution. Due to its high-level formulation, a partitioning strategy is defined to represent the neighbours set of each joint for constructing convolution operations, whereas $\textbf{A}_l$ and $\textbf{I}_l$ are dismantled into three partitions $p$ (spatial configuration proposed by \cite{yan2018spatial}), so $\textbf{A}_l + \textbf{I}_l = \sum_p$ $\textbf{A}_{l_p}$. For a single frame at resolution level $l$, the graph convolution can be visualized in Fig. \ref{fig:st-gcn} (left), which is computed as:

\begin{equation}\label{eq:1}
   \mathcal{S}(\textbf{X}_l) = \sum ^{p} _{i=1}\mathbf{\Lambda}_{l_i}^{-\frac{1}{2}}\textbf{A}_{l_i}\mathbf{\Lambda}_{l_i}^{-\frac{1}{2}}\textbf{X}_l\textbf{W}_{l_i},
\end{equation}
where the degree matrix $\mathbf{\Lambda}_{l_p}^{ii} = \sum _j (\mathbf{A}_{l_p}^{ij})$ normalizes the adjacency matrix $\mathbf{A}_{l_p}$ through the number of edges attached to each joint node. $\textbf{W}_{l_p}$ denotes the stacked weight vectors for each partition group $p$ from resolution level $l$.\par

Since multiple graph convolutional layers are used, different layers may contain multilevel semantic information \cite{cheng2020skeleton, shi2019two, yan2018spatial}, and simply using $\textbf{A}_l$ in Eq.~\ref{eq:1} results in the same pre-defined spatial weight configuration to every layer. Hence, we also resort to a learnable weight matrix $\textbf{M}_l \in \mathbb{R}^{N_l \times N_l}$ (initialized as an all-one matrix) on each layer of both generator and discriminator. Thus, we can adaptively learn to optimize the spatial weight configuration of $\textbf{A}_l$, and Eq. \ref{eq:1} becomes:

\begin{equation}\label{eq:2}
   \mathcal{S}(\textbf{X}_l) = \sum ^{p} _{i=1}\mathbf{\Lambda}_{l_i}^{-\frac{1}{2}}(\textbf{A}_{l_i} \odot \textbf{M}_l)\mathbf{\Lambda}_{l_i}^{-\frac{1}{2}}\textbf{X}_l\textbf{W}_{l_i},
\end{equation}

Over the temporal axis, considering that consecutive frames define consecutive skeletons, one-dimensional kernels are used as the temporal graph convolution, which is applied after the spatial graph convolution (Fig. \ref{fig:st-gcn}). Finally, our spatiotemporal graph convolution at resolution $l$ is given by convolving the positional features joint-wise as:

\begin{equation}\label{eq:3}
   \mathcal{T}\big(\mathcal{S}(\textbf{X}_l)\big)= \mathcal{S}(\textbf{X}_l) * \textbf{w}_l,
\end{equation}
where $\textbf{w}_l \in \mathbb{R}^{1\times t \times C}$ is the temporal kernel at resolution $l$ with $t$ as the number of frames to be convolved in the kernel.

\subsection{Generative Adversarial Graph Convolutional Network}

Kinetic-GAN consists of a generative adversarial network composed of a graph convolutional-based generator and discriminator. As previously stated, we perform upsampling and downsampling on samples, respectively, in the generator and discriminator (see Fig. \ref{fig:updown}). Since each spatial graph resolution $\mathcal{G}_l$ has its corresponding adjacency matrix $\textbf{A}_l$, we compute Eq. \ref{eq:3} at any resolution level $l$ of both upsampling and downsampling streams.\par

\begin{figure}[t]
  \begin{center}
  \includegraphics[width=8.4cm]{images/updown_v2.pdf}
  \caption{\textbf{Graph upsampling and downsampling paths}. Graph pyramids applied by Kinetic-GAN, where the left side refers to NTU RGB+D \cite{shahroudy2016ntu} and NTU-120 RGB+D \cite{liu2019ntu}, and the right side shows the Human$3.6$M \cite{ionescu2013human3}. The red nodes represents the root node of the respective dataset.}
  \label{fig:updown}
  \end{center}
  \vspace{-1.5em}
\end{figure}

Typically, generative-based action synthesis methods feed a latent vector to the generator and apply auto-regressive techniques \cite{wang2020learning, yu2020structure}, or Gaussian processes \cite{yan2019convolutional} to capture the temporal relationships in a latent sequence. From a generative perspective, the multiplicative interactions across the latent sequence provoke the entangling of factors of variation \cite{chen2018isolating, desjardins2012disentangling}. We depart from this constraint and propose to synthesise human actions from a single latent point $\bm{z}$ as conventional image generators. However, considering the numerous variation factors in human actions, our generator starts with a non-linear mapping network to transform the latent code $\bm{z} \in \bm{\mathcal{Z}}$ to produce an intermediate latent space $\bm{\mathcal{W}}$. The rationale is to allow less entangled latent factors, as previously confirmed on image modelling \cite{karras2019style, karras2020analyzing, shen2020interpreting}, and consequently increasing the linearity of factors of variation, where the generation of realistic actions becomes easier for the generator. This phenomenon is verified in the ablation study.\par
The proposed generator will gradually increase the resolution of a single intermediate latent point $\bm{w}$ over the spatial and temporal dimensions. First, spatial graph upsampling is computed by introducing new vertices and assigning the corresponding connected joints' average values. One-dimensional interpolation is performed over the temporal axis to increase the number of frames in the skeleton sequence. The discriminator will distinguish between the synthesised actions from the real ones by gradually coarsening the input skeleton's graph, where the vertices are removed, and corresponding neighbours are reconnected. Following the upsampling and downsampling streams from Fig. \ref{fig:updown}, the resolution is increased and decreased as:

\begin{equation}\label{eq:4}
   \textbf{X}_{l+1} = \mathcal{T}\Big( \mathcal{S}\big( Up( \textbf{X}_l ) \big) \Big), \quad
   \textbf{X}_{l-1} = Down\Big( \mathcal{T}\big( \mathcal{S}( \textbf{X}_l ) \big) \Big)
\end{equation}

Additionally, we propose to use residual blocks performing a skip connection with solely a temporal graph convolution to learn the temporal mappings more efficiently, which improves training stability and reduces the appearance of artefacts (we verify this phenomenon in the ablation study). Hence, we define our generator and discriminator residual block as:

\begin{equation}\label{eq:5}
    \begin{split}
       \textbf{X}_{l+1} &= \mathcal{T}\Big( \mathcal{S}\big( Up( \textbf{X}_l ) \big) \Big) + \mathcal{T}\big( Up(\textbf{X}_l) \big)\\
       \textbf{X}_{l-1} &= Down\Big( \mathcal{T}\big( \mathcal{S}( \textbf{X}_l ) \big) + \mathcal{T}(\textbf{X}_l) \Big)
    \end{split}
\end{equation}

\subsection{Conditional Adversarial Training}
The proposed architecture can be extended to a conditional model by feeding additional information about factors that we aim to condition. The generation of desired actions is imperative in human action synthesis; thus, we provide the embedded class information of the action $\bm{y}$ to both generator and discriminator. Specifically, in the generator, the embedded class representation $\bm{y}$ is concatenated to the prior input noise $\bm{z}$ before being mapped to the intermediate latent space $\bm{\mathcal{W}}$. The discriminator is fed with the channel-wise concatenation of the skeleton with the embedded class representation $\bm{y}$. In this paper, we rely on the WGAN-GP objective formulation \cite{gulrajani2017improved}, which is conditioned as:

\begin{equation}
\label{eq:6}
\begin{split}
\min_G \max_D \quad &\overbrace{\mathbb{E}_{\bm{x} \sim \mathbb{P}_r} \left[D(\bm{x} | \bm{y})\right] - \mathbb{E}_{\Tilde{\bm{x}} \sim \mathbb{P}_g} \left[D(\Tilde{\bm{x}} | \bm{y})\right]}^{\text{Discriminator loss}}\\
+\lambda \: &\overbrace{\mathbb{E}_{\hat{\bm{x}} \sim \mathbb{P}_{\hat{\bm{x}}}} [\left(\| \nabla_{\hat{\bm{x}}} D\left(\hat{\bm{x}} | \bm{y}\right)\|_2 - 1 \right)^2]}^{\text{Gradient penalty}},
\end{split}
\end{equation}
where $\mathbb{P}_r$ is the data distribution and $\mathbb{P}_g$ is the model distribution implicitly defined by $\Tilde{\bm{x}} = G(\bm{z},\bm{y}), \bm{z} \sim p(\bm{z})$ (the input $\bm{z}$ is sampled from a noise distribution $p$, which is then concatenated with the embedded action class representation $\bm{y}$). $\mathbb{P}_{\hat{\bm{x}}}$ is sampled uniformly along straight lines between pairs of points sampled from the data distribution $\mathbb{P}_r$ and generator distribution $\mathbb{P}_g$. The loss weight $\lambda$ for gradient penalty is set to 10 in all experiments.

\subsection{Improving Quality and Diversity}
\subsubsection{Stochastic Variation}
As previously stated, the current state-of-the-art methods are still attached to autoregressive techniques and Gaussian processes. Such procedures performed over the latent space reduce the ability of variation between samples.\par
Aside from the non-linear mapping network to attain less entangled latent factors, we propose an individual stochastic variation to circumvent this issue without affecting the skeleton structure in the action sequence itself. Specifically, we add random noise to each joint independently in the generator after each spatiotemporal graph convolution, which can be learned by assigning weights to every channel. Our noise injection operation is introduced in the generator's residual block as:

\begin{equation}\label{eq:7}
   \textbf{X}_{l+1} = \mathcal{T}\Big( \mathcal{S}\big( Up( \textbf{X}_l ) \big) \Big) + \mathcal{T}\big( Up(\textbf{X}_l) \big) + \bm{r}_{l+1}\textbf{w}_{l+1},
\end{equation}
where $\bm{r}_{l+1}$ denotes the Gaussian random noise for each joint to be added at resolution level $l+1$, and $\textbf{w}_{l+1}$ is the respective weight vector for each channel. The rationale is to provide a second input to the generator, which is handled to produce variation between samples without forcing the generator to use earlier activations from the latent space to generate random noise. Moreover, each layer has a corresponding per-channel weight $\textbf{w}_{l}$ and receives a new random noise $\bm{r}_{l}$, giving the flexibility needed to adaptively learn to produce stochastic variation without compromising the skeleton structure of the action (this phenomenon is verified in the ablation study).\par

\begin{figure}[t]
  \begin{center}
  \includegraphics[width=8.4cm]{images/blender/stochastic_walk_v3.png}
  \caption{\textbf{Examples of stochastic variation}. a) and b) Two human skeletons at the same frame from two action sequences (walking) generated by the same latent point. Zoom-in areas correspond to the respective coloured skeleton. c) Standard deviation over 100 different realizations from the respective frame, highlighting the skeleton parts affected by the noise. d) Cumulative standard deviation of each joint w.r.t. c). A cohesive behaviour is reproduced, where naturally the edges of limbs (hands, fingers, heels, feet) have a higher deviation than their parent joints.}
  \label{fig:stochastic}
  \end{center}
  \vspace{-1.7em}
\end{figure}

Fig.~\ref{fig:stochastic} illustrates the effect of stochastic variation on a walking sequence generated from the same latent point with different noise realizations. \ref{fig:stochastic} a) and b) show how noise affects different parts of the skeletons at the same frame, and c) highlights the standard deviation from 100 different realizations at the same frame, where most affected areas are the legs and arms (w.r.t. the walking action). The lower plot d) comprises a quantitative view of the standard deviation of each joint in c). It can be seen that the noise injector correctly learned the intrinsic natural variation of limbs since it affects more the edges, such as hands and feet than their corresponding parent joints (knees and elbows). Moreover, left limbs (blue) are slightly higher than their corresponding right ones (red) since the respective frame of the skeleton is moving the left leg forward, so naturally, its deviation will be higher than the right leg. For instance, in a throwing action, the arm that throws the object will always have more variation than the other arm. See accompanying video.

\subsubsection{Reducing Spatial Artefacts}
Typically, the increased quality of generated samples leads to unpleasant artefacts, which is a phenomenon already well-known in image modelling \cite{brock2018large,galteri2017deep,karras2020analyzing,wang2018esrgan}. Those are often the result of normalization methods over the generator, which improves training stability by eliminating covariate shift. Even so, the feature map's normalization omits any information concerning the individual feature's magnitude. It is assumed that the generator magnifies those magnitudes, and through the normalization process, it becomes unnoticed by the discriminator \cite{ karras2020analyzing, wang2018esrgan}.\par
This hypothesis is supported by the fact that removing these normalizations from our generator eradicated the appearance of artefacts. Still, it disabled our ability to manipulate human actions. Therefore, we found that reducing the number of layers that employ batch normalization led to better stability in action conditioning while completely removing artefacts. Fig. \ref{fig:artefact} illustrates an artefact example in action synthesis.

\begin{figure}[h]
  \begin{center}
  \includegraphics[width=8.4cm]{images/artefact.pdf}
  \caption{\textbf{Spatial artefacts in action synthesis}. First row shows an occurrence of spatial artefacts on a jumping action sequence generated by configuration {\small{(A)}} in Table \ref{tab:ablation_full}. Similar to image modelling, the remaining information is unaffected as we can identify a human skeleton jumping. Second row shows a jumping action sequence with corrected artefacts by configuration {\small{(C)}} in Table \ref{tab:ablation_full}.}
  \label{fig:artefact}
  \end{center}
  \vspace{-2.5em}
\end{figure}

\subsubsection{Truncation Trick on $\mathbfcal{W}$}
As described, the Kinetic-GAN samples $\bm{z}$ from $\mathcal{N}(0,1)$ and maps it an intermediate representation $\mathbfcal{W}$ of that distribution, which is then fed to the generator. So naturally, ranges of low density (in the training data) are not well represented, becoming difficult to learn for the generator, resulting in an important open problem in generative algorithms.\par
As previously confirmed, sample quality can be improved from truncated \cite{brock2018large,karras2019style,marchesi2017megapixel}, or shrunken \cite{kingma2018glow} sampling spaces. Thus, despite some variation losses, we follow the same approach to balance sample quality and diversity. During inference time, we scale the deviation of a given intermediate latent point $\bm{w}$ from the centre mass of $\mathbfcal{W}$ as:

\begin{equation}\label{eq:8}
   \bm{w}' = \mathbb{E}_{\bm{z} \sim \mathbb{P}_{\bm{z}}} \left[f(\bm{z})\right] + \psi(\bm{w} - \mathbb{E}_{\bm{z} \sim \mathbb{P}_{\bm{z}}} \left[f(\bm{z})\right]),
\end{equation}
where $\psi \leq 1$, $f(\cdot)$ denote our mapping network, and $\mathbb{P}_{\bm{z}}$ is the latent space distribution from 1000 points. Despite Brock \etal~\cite{brock2018large} reporting that only a subset of networks is manageable to such truncations even when orthogonal regularization is used, truncation in our intermediate space $\mathbfcal{W}$ successfully works even without adjustments to the loss function. As shown in Fig.~\ref{fig:trunc}, we can increase the generation quality in both benchmarks of NTU RGB+D \cite{shahroudy2016ntu}. However, for  $\psi \leq 0.9$, the variation starts to decrease and, consequently, the FID starts to increase.

\begin{figure}[t]
  \begin{center}
  \includegraphics[width=8.4cm]{images/trunc_evol.pdf}
  \caption{\textbf{Improvements through the ''truncation trick''}. The horizontal lines correspond to the respective FID obtained in Table \ref{tab:ablation_full} without truncations. All action sequences illustrated in this paper uses $\psi = 0.95$.}
  \label{fig:trunc}
  \end{center}
  \vspace{-1.8em}
\end{figure}

\section{Experiments and Discussion}
\label{sec:Results}
In this section, the experiments are split into local and global movement settings concerning if the 3D locations were positionally normalized into 2D space or not. The ablation studies verify the efficacy of the proposed model's properties over global movement. Then, our best performing model is compared to the state-of-the-art approaches over four datasets: NTU RGB+D \cite{shahroudy2016ntu} and NTU-120 RGB+D  \cite{liu2019ntu} (global movement) and Human$3.6$M \cite{ionescu2013human3} and NTU-2D RGB+D \cite{shahroudy2016ntu} (local movement).

\subsection{Datasets and evaluation metrics}
\textbf{NTU RGB+D} \cite{shahroudy2016ntu}. This dataset is composed of 56,880 video samples with 60 action classes. 3D skeleton data from 40 volunteers are provided for each action sample, with 25 joints for each skeleton. Authors recommend two benchmarks: 1) cross-subject, where models are trained with 20 subjects and tested with the remaining ones; and 2) cross-view, where models are trained with camera views 2 and 3 and tested on camera view 1. A curated version of this dataset is also used (2D joints locations, global to local movement normalization and selected samples) for a fair comparison in Table \ref{tab:local}, denoted by NTU-2D RGB+D. \par

\textbf{NTU-120 RGB+D} \cite{liu2019ntu}. This dataset is an extended version of its predecessor, comprising 114,480 video samples with 120 action classes. Samples were captured with three camera views in 32 different setups and 106 volunteers with 25 body joints for each skeleton. Authors recommend two benchmarks: 1) cross-subject, where models are trained with 53 subjects and tested with the remaining ones, and 2) cross-setup, where models are trained from samples with even setup IDs and tested on odd setup IDs.\par

\textbf{Human$\mathbf{3.6}$M} \cite{ionescu2013human3}. This dataset is a more simplistic set with 2D human motions and 15 body joints for each skeleton. For a fair comparison between the state-of-the-art, the same pre-processing and settings as \cite{wang2020learning, yu2020structure} were followed with corresponding 10 action classes.\par

\textbf{Evaluation metrics}. Two evaluation metrics are used for estimating the quality of synthetic samples. Similar to image modelling, we use the Fréchet Inception Distance (FID) \cite{heusel2017gans}, measuring the distance between the real data distribution and the synthesised one, considering the output activations of a specific layer from an InceptionV3 network \cite{szegedy2016rethinking}. Additionally, we measure the Maximum Mean Discrepancy ($\text{MMD}$) between real and synthetic samples based on a two-sample test to measure the discrepancy of both distributions. The $\text{MMD}$ over motion dynamics corresponds to the average $\text{MMD}$ across each frame, denoted by $\text{MMD}_{a}$, and the MMD over whole sequences are indicated as $\text{MMD}_{s}$. For joints over 2D space, only the $\text{MMD}$ is used.\par

\iffalse
\textbf{Experimental settings}. The skeleton sequences' temporal length was normalized to $t = 64$ frames for NTU RGB+D and NTU-120 RGB+D. Over Human$3.6$M and NTU-2D RGB+D, we follow the same configuration as \cite{wang2020learning, yu2020structure} w.r.t. the data normalization ($50$ frames, $10$ actions and global to local movement normalization). We set the learning rate to $0.0002$ for all datasets, but we found that setting the learning rate to 0.0003 leads to better stability for 32, 64, and 128 frames with Human$3.6$M. For our discriminator, batch normalization is not used, and used on our generator when no spatial upsampling is used. Mapping network depth is studied in Table \ref{tab:mapping}, where all weights are initialized using $\mathcal{N}(0,1)$.
\fi

\subsection{Global Movement Settings}
\label{sec:global}

\subsubsection{Ablation study}
\label{ssec:ablation}
Before diving into state-of-the-art comparisons, we first demonstrate experimentally that Kinetic-GAN properties improve sample quality considerably.\par

\newcolumntype{C}[1]{>{\centering\arraybackslash}p{#1}}
\newcolumntype{l}[1]{>{\arraybackslash}p{#1}}
\renewcommand{\arraystretch}{0.9}
\def\textBF#1{\sbox\CBox{#1}\resizebox{\wd\CBox}{\ht\CBox}{\textbf{#1}}}

\begin{table}[h]
\begin{center}
\begin{tabular}{|C{0.001\textwidth}|l{0.097\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|}
\cline{2-8}
\multicolumn{1}{c|}{} &
\textbf{\footnotesize{Method}} &
\textbf{\tiny{$\mathbf{FID}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{a}}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{s}}$}} &
\textbf{\tiny{$\mathbf{FID}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{a}}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{s}}$}} \\
\cline{2-8}
\noalign{\smallskip}
\hline
\parbox[t]{1mm}{\multirow{8}{*}{\hspace{-0.21em}\rotatebox[origin=c]{90}{\footnotesize{\textbf{NTU RGB+D}}}}}
& \multicolumn{1}{c|}{\cellcolor{gray!25}} &
\multicolumn{3}{c|}{\cellcolor{gray!25}  \footnotesize{\emph{Cross-Subject} }}&
\multicolumn{3}{c|}{\cellcolor{gray!25}  \footnotesize{\emph{Cross-View} }}\\
&\scriptsize{c-GAN \cite{mirza2014conditional} } &
\negthinspace\negthinspace\scriptsize{$27.480$}  &
\negthinspace\scriptsize{$0.919$}   &
\negthinspace\scriptsize{$0.975$} &
\negthinspace\negthinspace\scriptsize{$31.875$}   &
\negthinspace\scriptsize{$0.993$}    &
\negthinspace\scriptsize{$1.088$} \\
&\scriptsize{Baseline \cite{yan2019convolutional} } &
\negthinspace\scriptsize{$6.030$}   &
\negthinspace\scriptsize{$0.873$}    &
\negthinspace\scriptsize{$0.954$} &
\negthinspace\scriptsize{$7.114$}   &
\negthinspace\scriptsize{$0.910$}    &
\negthinspace\scriptsize{$0.991$} \\
\cline{2-8}
&\scriptsize{A \hspace{0.6em} Proposed} &
\negthinspace\scriptsize{$5.621$}  &
\negthinspace\scriptsize{$0.836$}&
\negthinspace\scriptsize{$0.927$}   &
\negthinspace\scriptsize{$6.528$}      &
\negthinspace\scriptsize{$0.883$}    &
\negthinspace\scriptsize{$0.953$} \\
&\scriptsize{B + No Residual} &
\negthinspace\negthinspace\scriptsize{$19.723$}   &
\negthinspace\scriptsize{$0.892$}     &
\negthinspace\scriptsize{$0.961$}&
\negthinspace\negthinspace\scriptsize{$21.331$}   &
\negthinspace\scriptsize{$0.971$}    &
\negthinspace\scriptsize{$1.030$} \\
&\scriptsize{C + Regular BN} &
\negthinspace\scriptsize{$4.751$}   &
\negthinspace\scriptsize{$0.815$}    &
\negthinspace\scriptsize{$0.917$}    &
\negthinspace\scriptsize{$5.328$}    &
\negthinspace\scriptsize{$0.867$}    &
\negthinspace\scriptsize{$0.940$} \\
&\scriptsize{D + Noise Inject} &
\negthinspace\scriptsize{$4.698$}   &
\negthinspace\scriptsize{$0.811$}&
\negthinspace\scriptsize{$0.895$}   &
\negthinspace\scriptsize{$5.102$}     &
\negthinspace\scriptsize{$0.851$}    &
\negthinspace\scriptsize{$0.933$} \\
&\scriptsize{E + Mapping Net} &
\negthickspace\scriptsize{$\mathbf{3.618}$} &
\negthickspace\scriptsize{$\mathbf{0.772}$}  &
\negthickspace\scriptsize{$\mathbf{0.871}$}    &
\negthickspace\scriptsize{$\mathbf{4.235}$}     &
\negthickspace\scriptsize{$\mathbf{0.824}$}    &
\negthickspace\scriptsize{$\mathbf{0.913}$} \\\hline

\end{tabular}
\vspace{-1em}
\end{center}
\caption{\textbf{Evaluating different generator designs}. The FID and MMD scores (lower is better) between real and synthetic samples generated under global body movement.}
\label{tab:ablation_full}
\end{table}

\textbf{Different generator designs}. In Table \ref{tab:ablation_full}, we compare the FID and MMD for various generator architectures in both benchmarks of NTU RGB+D, evaluating each distribution under the respective settings of each method. While the baseline (CSGN \cite{yan2019convolutional}) generates local movement in 3D space, we generate global movement and still exceed their performance by a significant margin. In CSGN \cite{yan2019convolutional}, the application of Gaussian processes limits their generation diversity due to the multiplicative interactions across each dimension of the latent space, which emulate the entangling of factors of variation. Additionally, the application of such processes has a high computational cost.\par

We start with our proposed configuration {\small{(A)}} with conditional sampling applying temporal skip connections and batch normalization over each generator layer. We then confirm the importance of residual blocks in our method by removing them from the generator  {\small{(B)}}, which clearly reduces sample quality, mainly due to training instability and, consequently, the appearance of artefacts. Since configuration {\small{(A)}} also generates occasional artefacts and eradicating batch normalization sacrificed our ability to generate desirable actions, we propose to regularize the use of batch normalization {\small{(C)}} by removing it when spatial upsampling is performed. This allows us to eliminate artefacts and have complete control to produce desirable actions while still improving sample quality compared to {\small{(A)}}. We also introduce noise injection {\small{(D)}} that further improves the results by adding even more diversity. Finally, we also include our mapping network {\small{(E)}}, where we distinctly overcome previous baselines and configurations due to the better disentangled latent space. \par

\begin{table}[h]
\begin{center}
\begin{tabular}{|C{0.001\textwidth}|l{0.098\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|}
\cline{2-8}
\multicolumn{1}{c|}{} &
\textbf{\footnotesize{Method}} &
\textbf{\tiny{$\mathbf{FID}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{a}}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{s}}$}} &
\textbf{\tiny{$\mathbf{FID}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{a}}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{s}}$}} \\
\cline{2-8}
\noalign{\smallskip}
\hline
\parbox[t]{1mm}{\multirow{7}{*}{\hspace{-0.21em}\rotatebox[origin=c]{90}{\footnotesize{\textbf{NTU RGB+D}}}}}
& \multicolumn{1}{c|}{\cellcolor{gray!25}} &
\multicolumn{3}{c|}{\cellcolor{gray!25}  \footnotesize{\emph{Cross-Subject} }}&
\multicolumn{3}{c|}{\cellcolor{gray!25}  \footnotesize{\emph{Cross-View} }}\\
&\scriptsize{ D Mapping Net $\emptyset$} &
\negthinspace\scriptsize{$4.698$}  &
\negthinspace\scriptsize{$0.811$}&
\negthinspace\scriptsize{$0.895$}   &
\negthinspace\scriptsize{$5.102$}&
\negthinspace\scriptsize{$0.851$}    &
\negthinspace\scriptsize{$0.933$}  \\
\cline{2-8}
&\scriptsize{\hspace{0.67em}  Mapping Net 1} &
\negthinspace\scriptsize{$4.049$}  &
\negthinspace\scriptsize{$0.792$}     &
\negthinspace\scriptsize{$0.892$}&
\negthinspace\scriptsize{$4.792$} &
\negthinspace\scriptsize{$0.847$}    &
\negthinspace\scriptsize{$0.931$} \\
&\scriptsize{\hspace{0.67em} Mapping Net 2} &
\negthinspace\scriptsize{$3.987$}  &
\negthinspace\scriptsize{$0.780$}     &
\negthinspace\scriptsize{$0.885$}&
\negthinspace\scriptsize{$4.698$} &
\negthinspace\scriptsize{$0.839$}    &
\negthinspace\scriptsize{$0.929$} \\
&\scriptsize{E Mapping Net 4} &
\negthickspace\scriptsize{$\mathbf{3.618}$}  &
\negthickspace\scriptsize{$\mathbf{0.772}$}  &
\negthickspace\scriptsize{$\mathbf{0.871}$}    &
\negthinspace\scriptsize{$4.473$}  &
\negthinspace\scriptsize{$0.831$}    &
\negthinspace\scriptsize{$0.926$} \\
&\scriptsize{E Mapping Net 6} &
\negthinspace\scriptsize{$3.849$}  &
\negthinspace\scriptsize{$0.801$}  &
\negthinspace\scriptsize{$0.889$}    &
\negthickspace\scriptsize{$\mathbf{4.235}$}  &
\negthickspace\scriptsize{$\mathbf{0.824}$}  &
\negthickspace\scriptsize{$\mathbf{0.913}$} \\
&\scriptsize{\hspace{0.67em} Mapping Net 8} &
\negthinspace\scriptsize{$4.396$}  &
\negthinspace\scriptsize{$0.805$}     &
\negthinspace\scriptsize{$0.891$}&
\negthinspace\scriptsize{$4.610$}&
\negthinspace\scriptsize{$0.837$}    &
\negthinspace\scriptsize{$0.919$} \\\hline

\end{tabular}
\vspace{-1em}
\end{center}
\caption{\textbf{Importance of the mapping network}. The number in method indicates the depth of the mapping network.}
\label{tab:mapping}
\end{table}

\textbf{Mapping network effectiveness}. The importance of the mapping network is presented in Table \ref{tab:mapping}, where we compare configuration {\small{(D)}} (not using a mapping network) with the increased number of layers in the mapping network. The cross-view benchmark required a deeper network than the cross-subject to attain the optimal performance. We justify this phenomenon due to the increasing number of different subjects in the data, which results in a more complex latent space representation. This indeed confirms the importance of the mapping network since naturally, if there is a greater number of different people, the variations will be higher.

\subsubsection{Synthesising 120 different actions}
\label{ssec:120actions}
Apart from our ablation studies in Section \ref{ssec:ablation} which were produced considering global movement settings, Table \ref{tab:ntu120} also presents the performance obtained by Kinetic-GAN over the NTU-120 RGB+D with 120 different actions, being currently the most extensive and challenging set with 3D joints annotations. Despite achieving a significantly better quality than previous methods, the ability to generate 120 different actions under global movement settings is a substantial improvement in comparison with previous state-of-the-art where they could only generate under local movement settings, and just 10 different actions \cite{habibie2017recurrent, wang2020learning,yu2020structure}.

\begin{table}[h]
\begin{center}
\begin{tabular}{|C{0.001\textwidth}|l{0.097\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|C{0.03\textwidth}|}
\cline{2-8}
\multicolumn{1}{c|}{} &
\textbf{\footnotesize{Method}} &
\textbf{\tiny{$\mathbf{FID}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{a}}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{s}}$}} &
\textbf{\tiny{$\mathbf{FID}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{a}}$}} &
\negthinspace\negthinspace\textbf{\tiny{$\mathbf{MMD_{s}}$}} \\
\cline{2-8}
\noalign{\smallskip}
\hline
\parbox[t]{1mm}{\multirow{3}{*}{\hspace{-0.21em}\rotatebox[origin=c]{90}{\footnotesize{\textbf{NTU-120}}}}}
& \multicolumn{1}{c|}{\cellcolor{gray!25}} &
\multicolumn{3}{c|}{\cellcolor{gray!25}  \footnotesize{\emph{Cross-Subject} }}&
\multicolumn{3}{c|}{\cellcolor{gray!25}  \footnotesize{\emph{Cross-Setup} }}\\
&\scriptsize{c-GAN \cite{mirza2014conditional}} &
\negthinspace\negthinspace\scriptsize{$54.403$}   &
\negthinspace\scriptsize{$1.037$} &
\negthinspace\scriptsize{$1.104$}   &
\negthinspace\negthinspace\scriptsize{$58.531$}   &
\negthinspace\scriptsize{$1.082$}   &
\negthinspace\scriptsize{$1.141$}   \\
&\scriptsize{\textbf{Kinetic-GAN}} &
\negthickspace\scriptsize{$\mathbf{5.967}$}  &
\negthickspace\scriptsize{$\mathbf{0.819}$}  &
\negthickspace\scriptsize{$\mathbf{0.906}$}   &
\negthickspace\scriptsize{$\mathbf{6.751}$}   &
\negthickspace\scriptsize{$\mathbf{0.847}$}   &
\negthickspace\scriptsize{$\mathbf{0.934}$}   \\
\hline

\end{tabular}
\vspace{-1em}
\end{center}
\caption{\textbf{Global movement generation results} on NTU-120 RGB+D with 120 different actions.}
\label{tab:ntu120}
\vspace{-1em}
\end{table}

\subsection{Local Movement Settings}
\label{sec:local}
Since most state-of-the-art methods were still limited to local movement settings, we also present the results under the same conditions for a fair comparison, where 3D locations were projected into 2D space and normalized positions. We follow the same settings as previous methods  \cite{habibie2017recurrent, wang2020learning,yu2020structure} and report the results on Table \ref{tab:local}. On both datasets, we verify the superiority of our model, where we achieve state-of-the-art performance with a large margin.

\newcolumntype{C}[1]{>{\centering\arraybackslash}p{#1}}
\newcolumntype{l}[1]{>{\arraybackslash}p{#1}}

\begin{table}[h]
\begin{center}
\begin{tabular}{|C{0.001\textwidth}|l{0.12\textwidth}|C{0.125\textwidth}|C{0.125\textwidth}|}
\cline{2-4}
\multicolumn{1}{c|}{} &
\textbf{\footnotesize{Method}}  &
\textbf{\tiny{$\mathbf{MMD_{a}}$}} &
\textbf{\tiny{$\mathbf{MMD_{s}}$}} \\
\cline{2-4}
\noalign{\smallskip}
\hline
\parbox[t]{1mm}{\multirow{9}{*}{\hspace{-0.21em}\rotatebox[origin=c]{90}{\footnotesize{\textbf{Human$\mathbf{3.6}$M}}}}}
&\scriptsize{E2E \cite{villegas2018hierarchical}} &
\scriptsize{$0.991$}   &
\scriptsize{$0.805$} \\
&\scriptsize{EPVA \cite{villegas2018hierarchical}} &
\scriptsize{$0.996$}   &
\scriptsize{$0.806$} \\
&\scriptsize{adv-EPVA \cite{villegas2018hierarchical}} &
\scriptsize{$0.977$}   &
\scriptsize{$0.792$} \\
&\scriptsize{SkeletonVAE \cite{habibie2017recurrent}} &
\scriptsize{$0.452$}  &
\scriptsize{$0.467$}  \\
&\scriptsize{SkeletonGAN \cite{cai2018deep}} &
\scriptsize{$0.419$}  &
\scriptsize{$0.419$}  \\
&\scriptsize{c-SkeletonGAN \cite{wang2020learning}} &
\scriptsize{$0.195$}   &
\scriptsize{$0.218$} \\
&\scriptsize{c-GAN \cite{mirza2014conditional}} &
\scriptsize{$0.161$}   &
\scriptsize{$0.187$} \\
&\scriptsize{SA-GCN \cite{yu2020structure}} &
\scriptsize{$0.146$}   &
\scriptsize{$0.134$} \\
&\scriptsize{\textbf{Kinetic-GAN}} &
\scriptsize{$\mathbf{0.071}$} &
\scriptsize{$\mathbf{0.082}$} \\\hline

\end{tabular}

\begin{tabular}{|C{0.001\textwidth}|l{0.12\textwidth}|C{0.05\textwidth}|C{0.05\textwidth}|C{0.05\textwidth}|C{0.05\textwidth}|}
\noalign{\vspace{0.5\smallskipamount}}
\cline{3-6}
\multicolumn{2}{c|}{} &
\textbf{\tiny{$\mathbf{MMD_{a}}$}} &
\textbf{\tiny{$\mathbf{MMD_{s}}$}}&
\textbf{\tiny{$\mathbf{MMD_{a}}$}} &
\textbf{\tiny{$\mathbf{MMD_{s}}$}} \\
\cline{3-6}
\noalign{\smallskip}
\hline
\parbox[t]{1mm}{\multirow{7}{*}{\hspace{-0.21em}\rotatebox[origin=c]{90}{\footnotesize{\textbf{NTU-2D RGB+D}}}}}
& \multicolumn{1}{c|}{\cellcolor{gray!25}} &
\multicolumn{2}{c|}{ \cellcolor{gray!25}  \footnotesize{\emph{Cross-Subject} }}&
\multicolumn{2}{c|}{ \cellcolor{gray!25}  \footnotesize{\emph{Cross-View} }}\\
&\scriptsize{SkeletonVAE \cite{habibie2017recurrent}} &
\scriptsize{$0.992$}   &
\scriptsize{$1.136$}  &
\scriptsize{$1.079$}   &
\scriptsize{$1.205$} \\
&\scriptsize{SkeletonGAN \cite{cai2018deep}} &
\scriptsize{$0.698$}   &
\scriptsize{$0.788$} &
\scriptsize{$0.999$}   &
\scriptsize{$1.311$}  \\
&\scriptsize{c-SkeletonGAN \cite{wang2020learning}} &
\scriptsize{$0.338$}   &
\scriptsize{$0.402$} &
\scriptsize{$0.371$}   &
\scriptsize{$0.398$}  \\
&\scriptsize{c-GAN \cite{mirza2014conditional}} &
\scriptsize{$0.334$}   &
\scriptsize{$0.354$}  &
\scriptsize{$0.365$}   &
\scriptsize{$0.373$} \\
&\scriptsize{SA-GCN \cite{yu2020structure}} &
\scriptsize{$0.285$}   &
\scriptsize{$0.299$}  &
\scriptsize{$0.316$}   &
\scriptsize{$0.335$} \\
&\scriptsize{\textbf{Kinetic-GAN}} &
\scriptsize{$\mathbf{0.256}$}  &
\scriptsize{$\mathbf{0.273}$}  &
\scriptsize{$\mathbf{0.295}$}   &
\scriptsize{$\mathbf{0.310}$}  \\\hline

\end{tabular}
\vspace{-1em}

\end{center}
\caption{\textbf{Local movement generation results}. The MMD scores (lower is better) between real and synthetic samples generated over Human$3.6$M and NTU-2D RGB+D.}
\label{tab:local}
\vspace{-1.7em}
\end{table}

\subsubsection{Increasing temporal action length}
\label{ssec:action_recon}
In addition to generation quality, action conditioning (desirable actions) and local/global movement, modelling long-term human actions is also a significant concept in action synthesis. However, NTU RGB+D could not be employed for such a study since the action execution average is 64 frames, where the remaining frames (maximum 300) are all set to 0 for normalization purposes. For this reason, such normalization can disturb the learning process of an action synthesis algorithm. So, we performed long-term experiments over Human$3.6$M, Fig.~\ref{fig:time}. Considering that our model can perform \textit{bidirectional temporal dependency}, mainly due to generating a whole human action sequence altogether, we can generate up to 1024 frames (34 seconds). Some autoregressive models \cite{fragkiadaki2015recurrent, zhou2018auto} gradually freeze poses during the sequence as a result of losing temporal dependencies for such sequence lengths. It can also be observed that its generation quality will stabilize over lengths $>256$ frames, which can also be positively regarded.

\begin{figure}[t]
  \begin{center}
  \includegraphics[width=8.4cm]{images/time_evol.pdf}
  \caption{\textbf{Increasing action length} on Human$3.6$M.}
  \label{fig:time}
  \end{center}
\vspace{-1.8em}
\end{figure}

 \section{Conclusions and Further Work}
 \label{sec:Conclusions}
This paper introduced a novel Generative Adversarial Graph Convolutional Network for human action synthesis. By generating a human action in a holistic way directly from the latent space, we are able to better disentangle the variation factors through a mapping network, obtaining better representations in the latent space. Furthermore, the introduction of learnable noise injection modules facilitates the generation of variety without compromising the skeleton structure. As a result, we can generate up to 120 different complex actions, which, to the best of our knowledge, were particularly challenging for previous approaches under global movement settings. The proposed method was evaluated on three well known datasets (NTU RGB+D, NTU-120 RGB+D and Human$3.6$M), advancing the state-of-the-art performance metrics by a significant margin.

\footnotesize{\textbf{Acknowledgements}: This work was partially supported by the FCT/MEC through National Funds and by the FEDER-PT2020 Partnership Agreement under the Projects UIDB/50008/2020, POCI-01-0247-FEDER-033395, CENTRO-01-0247-FEDER-113023 - DeepNeuronic, operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competências em Cloud Computing, co-funded by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio a Investiga\c{c}\~{a}o Cient\'{i}fica e Tecnol\'{o}gica - Programas Integrados de IC\&DT and NOVA LINCS under grant 'UIDB/04516/2020'. This research was also supported by 'FCT - Funda\c{c}\~{a}o para a Ci\^{e}ncia e Tecnologia' through the research grant 'UI/BD/150765/2020' and `2020.04588.BD'. }

\normalsize

redpajama_set_name: RedPajamaArXiv


"Since the creation of the Federal Reserve, middle and working-class Americans have been victimized by a boom-and-bust monetary policy. In addition, most Americans have suffered a steadily eroding purchasing power because of the Federal Reserve's inflationary policies. This represents a real, if hidden, tax imposed on the American people.
Nullify The Private Federal Reserve: Whether Our Non Representative Congress Wants Us To Or Not!
(CNSNews.com) – Since President Barack Obama was inaugurated in January 2009, the Federal Reserve's holdings of U.S. government debt have quintupled, according to the Fed's official monthly balance sheet.
On Jan. 28, 2009, a week after Obama's nomination, the Fed owned $302 billion in U.S. Treasury securities. On April 25, 2012, the latest date reported, the Fed owned five and a half time that much in U.S. Treasury securities–$1.668 trillion.
Obama and all the supporters of the resent Dodd/Frank Financial-Overhaul billonly put more Power and Control into the Hands of the Federal Reserve and want U.S. to perceive it as good for U.S.
Over $700,000,000,000.00 a Year Interest on Our Money Created AS DEBT Out Of Thin Air!
Since 1913 these freeloaders of Society who have profited undeservedly from Our labors, have only repaid U.S. by corrupting Our government. They want complete Power and Control over U.S. and Our children.
— Henry Ford, founder of the Ford Motor Company.
Will Romney Erase The Debt & Arrest The Corrupt Politicians & Bankers Like Iceland Has?
Ron Paul: Repeal The 1913 Rothschild Federal Reserve: The Hidden Treasonists!
← States Enforcing The United States Constitution: Florida Moves To Nullify Obamacare & Supreme Court Justice Roberts!


text:
security IG explains why kidnapping occurs in schools…says Evans' trial will be fair
IG explains why kidnapping occurs in schools…says Evans' trial will be fair
by Olorunsomo Smart on August 04, 2017 in security
The Inspector - General of Police , Ibrahim Idris, has described the incessant cases of kidnapping in the country as a complex task for the police to tackle .
Idris identified shortage of police manpower and the building of schools and houses in isolated areas as some of the factors that made kidnapping a difficult crime to tackle by the police .
The IG spoke during a courtesy visit to The PUNCH Place, headquarters of Punch Nigeria Limited on the Lagos - Ibadan Expressway on Thursday.
Responding to a question about kidnapping and killings by a notorious cult called Badoo, Idris said the police were working on improving security in the coastal areas between Lagos and Port Harcourt, the Rivers State capital , by acquiring more gunboats for patrol .
He, however , added that their job had been made more difficult because of the choice of some people to build houses and schools in desolate areas , especially in the bush.
He said, "We are trying to improve and enhance how we deal with criminality in the coastal areas , not only Lagos, but from here to Port Harcourt and one of our strategic plans is to better equip our marine police .
"We are trying to acquire more gunboats so that we can be very effective and visible in these coastal areas — somebody will build a school in an isolated bush. That is why we said when you are looking at criminality, you have to also look at some factors and try to address those factors as a whole and not always point at the police .
"There is a limit to what we can do ; if you go and build your house in the bush, we are talking of personnel , you cannot put policemen in every school . If we do that , we are going to have a problem in addressing other issues .
"There are some basic utilities that a school requires — closed- circuit television cameras . Put some things there to fortify the school. If the location is bad , make sure you fortify the school. You can have a security department ; hire more security men on the ground. But nobody looks at some of these factors . "
The IG also noted that more attention should be paid to dealing with delay in criminal trials in courts and factors that push youths into crime.
Speaking on the continued detention of an alleged kidnap kingpin, Chukwudumeme Onwuamadike , a. k. a . Evans , who had yet to be charged to court , Idris said the police deserved "credit" for the arrest , while promising that the suspected kidnapper would be given a fair trial.
Idris' visit to The PUNCH Place was with a large retinue of armed policemen and in his response to a question about that and the continued attachment of policemen to politicians vis - à- vis the inadequate provision of security for the public, the IG said it was necessary to protect people in leadership positions.
He said, "On the issue of personnel ; in reality, the Nigeria Police Force has about 298 , 000 policemen and women and I agree with you . You know, it is (like that ) all over the world , you have to protect the leadership of the country.
"Even in the US , up till date , when President Donald Trump is going anywhere , some roads have to be blocked and you need policemen to do that , you have to deploy the assets you have to protect the leadership because you know they are the symbols of the country . So, it is not only in Nigeria that if a president is going somewhere, you just (don' t) allow him to go like that . You know we had a bitter experience in the past, when (former military ruler ) Murtala Muhammed was assassinated. He stopped at a traffic light and was killed.
"It is natural for any country to accord the leadership that level of maximum protection because whether we like it or not , I am not in your position , I have so many enemies and not made by me , but through my people . "
Idris, who lamented the shortage of police personnel , said it was worsened because the country did not employ any rank and file member of the Force between 2010 and 2016 .
He, however , added that a bill that would guarantee improved funding for the police was before the National Assembly and awaiting the legislature's assent.
After initially expressing surprise to a question insinuating that Badoo was still operating in Lagos and Ogun states , he said, "It has not been dealt with, I 'm surprised. "
Idris, therefore, deflected the question to the Lagos State Commissioner of Police , Fatai Owoseni, who said the group had been dealt with.
Owoseni said, "Badoo has been dealt with . Before this last one (killing by a suspected members of Badoo), for the past six weeks, we didn 't have anything and such a crime is not what you can just stop because of the nature of the environment .
"All the places where the incidents happened were right inside the bush, so if you put one million policemen in that place , it will be difficult. These were people that just went to the middle of nowhere , put a house (there ) with no window, no door, nothing and nobody has seen Badoo face- to- face.
"So, we are dealing with that and we are improving. "And that is why we have built partnership with members of the community to get to the grassroots so that when they see (something suspicious), they can tell us . "
Source: Punch
Labels: security
OD Woods out with debut album "Ikonic" - Nigerian singer cum songwriter Orduen Ikon Andrew better known as OD Woods has just dropped his maiden studio album titled "Ikonic" after several years ...

redpajama_set_name: RedPajamaCommonCrawl


The score line doesn't do justice to the City youngsters' performance at 2nd placed Fulbourn.
From the whistle, both teams were looking to create chances as play swung from end to end, but, in the 14th minute, just as Fulbourn looked to be getting a firm grip on the game, Harry Farrow broke through the Fulbourn defence, cut inside and beat the onrushing keeper to make it Fulbourn 0 City 1.
City now had their tails up and were having more than their fair share of play. The speed of Harry Farrow, Christian Carter and Claudio Sousa, causing the Fulbourn defence problems.
In the 26th minute Tommy Rigby and Christian Carter worked well and Harry Farrow finished off the move, pulling a fine one-handed save out of the Fulbourn keeper, but the ball went straight back down the other end for Fulbournto hit the post.
The City defence, with captain Mapeo Mendes leading by example, were looking very solid but, in the 40th minute Fulbourn managed to break through and level the score.
City were playing attractive football at a good pace and continued to look dangerous, especially on the break, repeatedly stretching the Fulbourn defences, but they couldn't increase their lead and the half finished at: Fulbourn 1 City Development 1.
In the 2nd half the strength and experience of the home team finally shone through and after five minutes of constant pressure, Fulbourn took the lead.
Shortly afterwards they came close to increasing that lead when City stand-in keeper, ex-manager Andy Jolley and the cross bar juggled the ball between them, Andy finally falling on the ball inches from the line.
In the 65th minute it looked as though ex-City player Tommy Wright would put Fulbourn further ahead but his chip not only cleared keeper Jolley, but also the crossbar.
Ten minutes later it was a long range shot that dropped out of the setting sun and into the back of the net to give Fulbourn their third goal.
Fulbourn kept up the pressure, but City showed they were still in the game and continued to create attacking moves threatening to reduce the arrears.
With 2 minutes to go Fulbourn scored their final goal as this time, with just the keeper to beat, Tommy Wright made no mistake.
It was a good display from a promising young City team in an entertaining game played in good spirit. But the experience of the home side was the deciding factor.
City Team: Andy Jolley, James Skillen, Ben Starmer, Sam Collin, Mapeo Mendes (C), Matt Jenkins, Christian Carter, Finn Cater, Harry Farrow, Tommy Rigby, Claudio Sousa.
Subs: Tom Goodjohn, Morgan Cogman-Wright.


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Breaking Up is Hard To Do – Chapter 2
by Neema Amini • June 22, 2022 •
12+ minute read
In disputes involving musical groups and other entertainment "partnerships," there is a high probability that intellectual property will be involved. Consider the example of a musical group that has written and recorded songs together, or a financier and a filmmaker who have collaborated on a documentary. In such cases, copyright law generally will determine the party or parties in whom initial copyright ownership has vested and whether there has been a valid transfer of such rights. The preeminence of copyright law in this context is a function of the federal Copyright Act's preemption provision, which states that copyright law preempts any claim that seeks to vindicate "legal or equitable rights that are equivalent to any of the exclusive rights within the general scope of copyright". Because "authorship" is the "well" from which these rights spring, a disputed claim of ownership related to authorship will generally be determined by copyright law.
A Governing Law
Likewise, the Copyright Act has special provisions governing the valid transfer of ownership, so that these issues are typically governed by copyright law. Notwithstanding the foregoing, many disputes involving copyrighted material are guided by state law, including royalty disputes, contractual disputes, claims for accounting, conversion, partners' rights in copyrights owned by the partnership, and the like.
This is because the claimants in such cases generally are not seeking to enforce rights arising directly under the Copyright Act, nor would resolution of the central issues in their cases typically depend upon application of the Act. Indeed, where there is no dispute over authorship, transfer of copyright, or infringement of one of the exclusive rights set forth in 17 U.S.C. § 106, it has been suggested that there simply is no federal jurisdiction arising under the copyright law—even though copyrighted material may be at the center of the dispute.
Why is this so important? And why must an entertainment law practitioner be familiar with both state law (including partnership law) and copyright law?
Consider a fairly straightforward example: A self- produced recording artist and a financier enter into an oral partnership agreement to produce and sell records. The finaricier assumes that the copyrights in the sound recordings belong to the partnership because production was financed with partnership funds. To his dismay, he learns that the copyrights could not have become the property of the partnership without a written instrument of transfer or "work-for-hire" situation; and that his partner—as the sole "author" of the sound recordings—is therefore the sole owner of the copyrights.
Ownership in Copyrighted Works
There are two ways in which a partnership may acquire ownership in copyrighted material: (1) under the so-called "work-for-hire" doctrine 40 or (2) by means of written transfer of ownership.
There are three ways in which ownership of copyrights might vest in the individual partners—the first two of which are identical to the manner in which a partnership may acquire rights: (1) if the partner acquires the rights in his own name under the so- called "work-for-hire" doctrine, (2) if he acquires the rights in his own name" by means of written transfer of ownership, or (3) if he is an "author" or "co- author" of the work. These three issues are discussed in further detail below.
There are two categories of "work-for-hire": (1) a work created by an employee within the scope of his employment and (2) a "specially commissioned work," evidenced by written contract, in one of nine statutorily enumerated categories. Thus, if an employee creates a "work" during the course and scope of his employment, the copyright is presumptively owned by the employer. It is important to note that, because a partner ordinarily is not an "employee" of the partnership, works created by a partner in the scope of his duties for the partnership do not become partnership property under this doctrine. Without a written agreement conferring rights to the partnership, at most it will have acquired an implied license to use the work.
With respect to "specially commissioned works" for hire, the important issues to consider are: (1) whether there is a written contract evidencing the relationship and (2) whether the work falls under one of the nine statutorily enumerated categories of works subject to such protection. Absent both conditions, by definition there is no specially commissioned work- for-hire."
Transfer of Ownership
Rules applicable to transfer of copyright ownership are fairly straightforward. If there is a written instrument evidencing transfer of ownership, the transfer is valid. Otherwise, there is no transfer of ownership
Authorship and Co-Authorship
The most direct manner in which copyrights may be acquired—and perhaps the most common in situations involving musical groups and other informal entertainment partnerships—is by virtue of "authorship." The Copyright Act provides that initial ownership of copyrights vests in the "author or authors" of the same. The authors of a joint work are co-owners of copyright in the work.
Disputes Among Collaborators
In accordance with the foregoing principles, where a dispute has arisen among collaborators— unless a putative co-owner can prove that he gained rights by written contract (e.g., by written transfer or "work-for-hire")—the endgame will be the successful assertion of a claim of co-authorship. "In common with many issues arising in the domain of copyrights, the determination of whether to recognize joint authorship in a particular case requires a sensitive accommodation of competing demands advanced by at least two persons, both of whom have normally contributed in some way to the creation of a work of value. In other words, in most cases involving a dispute of co-authorship, both parties will have actually "collaborated" on the work.
The question is whether each collaborator is legally entitled to a copyright interest. A warning in advance: This area of copyright law is fraught with competing policy arguments, conflicting decisions, and differing tests in various federal circuits, or no developed position at all. Of course, this is what makes it all so interesting.
Under the plain language of the Copyright Act, "[t]he authors of a joint work are co-owners of the copyright in the work. A "joint work" is defined as "a work prepared by two or more authors with the intention that their contributions be merged into inseparable or interdependent parts of a unitary whole. That definition is easily stated, but its application has vexed many a court. Obvious questions arise. For instance, who qualifies as an "author?" Will any creative contribution suffice, or is something more required? The inquiry has not stopped there. When faced with a fact pattern that appears deserving of a more nuanced test of co-authorship, courts have asked additional questions.
For instance, regarding "intent," is it necessary that the parties fully comprehend the legal consequences of their relationship, or is it sufficient that the parties simply intend to create a unified work? When must this intent arise? Must it be contemporaneous with initial creation, or is subsequent intent to merge efforts sufficient? Some of these issues are fairly well settled. For better or worse, many are not.
Who Qualifies as an "Author"?
Given that a person's status as an "author" is a principal benchmark in determining both "ownership" of a work and whether a "joint work" has been created, one would expect to find a definition of the term in the Copyright Act. Incredibly, Congress failed to include one. As a result, definition of that pivotal term has become the unenviable job of the courts.
Independent Copyrightability
The Current Scenario Under the bulk of the relevant jurisprudence, each putative co-author must have made a contribution "that could stand on its own as the subject matter of copyright In other words, it is not enough that the end product may be copyrightable: for any contributor to claim a copyright interest, his contribution must have been "independently copyrightable."
This so-called rule of "independent copyrightability" may be appropriate in a number of settings: e.g., preventing those who have made no more than de minimus contributions from participating in the ownership of a work. However, there are other situations—particularly in the entertainment industry —in which a strict application might lead to the evisceration of copyright protection for persons making significant artistic and expressive contributions.
Consider, for example, a sound engineer who captures the live performance of a musical group, or a producer who collaborates with an artist in the studio. May the contributions of either of these parties be said to "stand on their own as the subject matter of copyright? Consider further the example of the drummer and bass player who, as part of a musical group, regularly contribute their creative efforts to songs brought in by the group's songwriters.
Should those persons occupy the status of "coauthors," or be relegated to some lesser status where only a written contract would insure remuneration? Under an ordinary definition of "authorship," all of these collaborators should be accorded co-authorship status. However, a number of courts have used the test of "independent copyrightability" to deny authorship in such cases.
Fully acknowledging the uphill battle, the Nimmer treatise has argued for some time against a strict requirement of independent copyrightability of each author's contribution.° Nimmer observes that neither the plain language of the Act nor its legislative history requires the conclusion that each putative author's contributions must be independently copyrightable.
Rather, as Nimmer notes, the intention to merge each putative author's contributions into a "unitary whole" appears to be the "touchstone. Nimmer notes that "the copyright's goal of fostering creativity is best served by rewarding all parties who labor together to unite idea with form."" As such, the test Nimmer would employ is whether the putative author's contributions were more than de minimus.
Gaiman v. McFarlane—A Relaxing Standard?
As indicated above, the bulk of the relevant jurisprudence adheres to Professor Goldstein's test, requiring' each putative author's contributions to be independently copyrightable. However, the landscape changed a bit in 2004 with the Seventh Circuit's decision in Gaiman v. McFarlane: which appears to have adopted Nimmer's interpretation of "co-authorship."
In Gaiman, the creator and illustrator of a comic book series invited the plaintiff to write a script for one issue: Subsequently, the plaintiff wrote a script in which he introduced three new characters to the series. Based on the plaintiff's oral description of the characters and his written dialogue, the creator/illustrator "drew" those characters into the series. Thereafter, plaintiff brought suit, contending that he was entitled to an ownership interest in the copyright to the three characters. by virtue of the Copyright Act's joint-work provision.
Although Judge Posner recognized that a contribution of "some original expression" is required for authorship status, he rejected the notion that such contributions must always be independently copyrightable.
He reasoned as follows: [W]here two or more people set out to create a character jointly in such mixed media as comic books and motion pictures and succeed in creating a copyrightable character, it would be paradoxical if though the result of their joint labors had more than enough originality and creativity to be copyrightable, no one could claim a copyright. That would be peeling the onion until it disappeared. The decisions that say, rightly in the generality of cases, that each contributor to a work must make a contribution that if it stood alone would be copyrightable weren't thinking of the case in which it couldn't stand alone because of the nature of the particular creative process that had produced it.
Applying this logic, Judge Posner concluded that the plaintiff's creative contributions, although not independently copyrightable, were sufficient to accord him authorship status. Regarding one of the comic book characters, Judge Possner noted that "Gaiman's contribution may not have been copyrightable by itself, but his contribution had expressive content without which [the character] wouldn't have been a character at all, but merely a drawing.""
In supplements to the Nimmer series after Judge Posner's opinion, one can sense a bit of triumphant vindication. Will this be the beginning of a trend? If a recent decision on the subject is any indication, the tide has not yet turned; however, curiosity and thoughtful judicial inquiry into the issue has certainly been piqued.
Fifth Circuit Stance
The Fifth Circuit has yet to announce whether "independent copyrightability" is a requisite element of joint authorship.85 As discussed above, one judge from the Eastern District of Louisiana has announced the requirement.
However, that decision predated Gaiman, and because it was a district court opinion, it has no binding precedential value on other trial judges. As such, practitioners are free to urge either side of the argument, and depending on who they represent, should certainly consider both approaches.
Must the Co-Authors Understand the Legal Consequences of Co-Authorship?
The Copyright Act requires putative authors to intend "that their contributions be merged into inseparable or interdependent parts of a unitary whole." There is nothing in that definition that requires a putative author to understand the legal consequences of joint authorship or to manifest that understanding by his conduct. Nonetheless, a number of courts—including several district courts within the Fifth Circuit—appear to have adopted these requirements. The trouble started with the Second Circuit's opinion in Childress v. Taylor.
That case involved a claim of co-authorship of a play about comedienne Jackie "Moms" Mabley. The putative co-author contracted the playwright to create the script, and allegedly contributed the results of her research into the comedienne's life." The district court denied plaintiff's claim of co-authorship, finding that the parties did not share the requisite intent to be co- authors.
On appeal, the Second Circuit observed that the Copyright Act limits its analysis of "intent" to the co- authors' "state of mind regarding the unique nature of the finished work." It nevertheless decided that this limited inquiry may not be sufficient in all cases, particularly those involving a "dominant author." In such cases, the court believed that it was necessary to plumb the co-authors' awareness and understanding of "the concept of joint authorship" and to adduce whether the putative authors had evidenced such an understanding through their conduct.
Although the court insisted that it was not requiring an understanding of "legal consequences," it would be difficult to imagine a situation in which the authors could evidence an understanding of the "concept" of co-authorship without some notion of the legal consequences. The most recent opinion emanating from a district court within the Fifth Circuit acknowledging the reasoning of Childress is BTE v. Bonnecaze.
In Bonnecaze, a former drummer for the musical group "Better than Ezra" claimed co-authorship in the group's sound recordings and musical compositions. The drummer's alleged authorship contributions were significant, consisting of "harmony, lyrics, percussion and song rhythms, melody and song and musical structure?
Despite the weighty nature of the alleged contributions, and the undeniable fact that collaborators merged their contributions into a "unitary whole" when they recorded the works, the court insisted that "a specific finding of [subjective] mutual intent remain[ed] necessary." The court did not clarify whether proof of the parties' understanding of the legal consequences of co-authorship was required; however its identification of the "factual indicia of ownership and authorship" suggests that it was. For instance, the court insisted that it was necessary to examine how the "parties bill[ed] or credit[ed] themselves with regard to the work," and whether the drummer had entered into "any written agreements with third parties involving the work."
The problem with this formulation of the "mutual intent" requirement is that it is impractical, particularly in situations involving informal partnerships such as musical groups. To put it mildly, musicians (and other artists) seldom comprehend all of the legal ramifications of their actions. It is therefore absurd to require them to acquire such an understanding (and to manifest that understanding in their affirmative actions) in order to enjoy co- authorship status.
A recent opinion from the Western District of Texas shows just how dangerously restrictive this approach can be. In Clogs/on v. American Academy of Orthopedic Surgeons,"" a photographer brought a claim of co-authorship, based upon his contribution of 90% of the photographs appearing in an instructional textbook comprising some 450 photographs.'" The court cited deposition testimony of the putative author in which he opined that the issue of "joint authorship" was a "legal question" (a perfectly reasonable response).
Based on that testimony, the court concluded that the photographer did not have the requisite intent to become a co-author.'" Obviously, that sort of logic is circular and is not in the least bit compelled by the Act itself which requires only that co-authors intend to merge their efforts into a "unitary whole." The plain language of the Copyright Act in fact mirrors the test first announced by an English court 100 years prior: "to constitute joint authorship, there must be a common design. Obviously, a "common design" is a far cry from a pre-manifested intent to enjoy the legal status of a "co-author."
Happily, a universal application of the "mutual intent" requirement set forth in Childress does not appear to have been suggested—even by that court. Thus, those courts that have slavishly applied it as 1114) strict requirement may have overreacted. Indeed, the Childress court's announcement of the "conceptual understanding" test was in response to concerns over special cases involving a dominant author, such as a "writer-editor" or "writer-researcher" relationship (the latter of which was precisely the case before the court).
Perhaps confined to those situations, the "conceptual understanding" requirement is helpful, but the problem with the test is that it quickly falls downhill into situations in which it has no business, including co- songwriter cases like Bonnecaze. As the Childress court properly noted, the test "requires less exacting consideration in the context of traditional forms of collaboration, such as between the creators of words and music of a song. Evidently, the court in Bonnecaze overlooked that portion of the opinion. Fortunately, other courts have been more observant of the limited application of the Childress test.
A good example is Systems, XIX, Inc. v. Parker. In that case, a recording studio brought a claim of co- authorship against the performer KRS-ONE and his record company, claiming rights in a live recorded performance that had been specifically requested and authorized by the performer's road manager. Citing Childress, the defendants raised the defense of "mutual intent," asserting that the parties did not share the "subjective intent" to be co-authors.
Noting that the Childress court itself suggested that its test should be applied only in circumstances "not likely to have been within the contemplation of Congress" and noting further that the producer/performer relationship had been under the "specific contemplation of Congress" when it enacted the Copyright Act, the court declined to apply the Childress test.
When Must "Intent" be Manifested?
The intent to merge contributions into a "unitary whole" must exist at the moment in time when the work is created, not at some later date. However, the rule is somewhat broader than it appears. First of all, the actual identity of a co-author does not need to be known, nor must the respective contributions be made at the same time. Thus, a lyricist who intends for his lyrics to be set to music may be considered a joint author of a composer who even years later, with the permission of the lyricist (or his agent), eventually sets the lyrics to music.
Here, it is instructive to go back to the example of the bass player and the drummer who, as part of a musical group, regularly contribute their creative efforts to songs brought in by the principal "songwriter." In this instance, the question of "intent" would appear to be relevant, perhaps determinative. If there is evidence that the songwriter "intended" for his song to be complete upon the addition of drums and bass, then one could argue that he had the requisite level of intent for joint authorship. On the other hand, if the evidence suggests that he intended for the song to stand on its own (e.g., as lyrics with guitar accompaniment only), the better argument might be that the songwriter lacked the intent to create a "unitary whole" with the bass player and drummer (although he may have intended to create a "derivative work").
Regardless, if the musicians subsequently collaborated on a recording of the song, a logical conclusion could be drawn that all parties intended to collaborate in a "unitary whole" (the "sound recording"). Thus, a claim of co-authorship in the sound recording would appear to be the easier argument.
Independent Fixation
In addition to the hard line it took on "intent," the Court in Bonnecaze announced that it is necessary for each putative coauthor to "fix" his contributions into a tangible form of expression in order to be entitled to co-authorship status. That conclusion would appear to be in error. Even Professor Goldstein — a strong proponent of the "independent copyrightability" requirement — opines that "it is not necessary to the creation of a joint work that each collaborator actually "fix" the work in a tangible medium of expression.
One could imagine the difficulties of applying such a strict test in the field of music, where musicians commonly write parts "by ear." In fact, that appears to have been the exact context in which the Bonnecaze court applied the rule. The court's denial of co- authorship was all the more curious given that the claimant did in fact "fix" his contributions in the group's sound recordings.
Rights of Co-Authors and Co-Owners
Absent a written agreement to the contrary, co- owners of a copyrighted work (including co-authors) share equally in the ownership of the work.'" In the case of co-authors, this is true regardless of whether their respective contributions to the joint work are equal. Indeed, one court has held that the varying of this general rule amounts to a "transfer" of copyright ownership that will be valid only if it is confirmed by a signed instrument.
A joint author (or joint owner) of a copyrighted work—regardless of his ownership percentage—may, without obtaining the consent of the other joint owners, either exploit the work himself, or grant nonexclusive licenses to third parties. However, he must "account to other co-owners for any profits he earns from the licensing or use of the copyright. This "duty to account does not derive from the copyright law's proscription of infringement. Rather, it comes from general principles of law governing the rights of co-owners." Inasmuch as these "joint tenancy" principles are rooted in state law, courts will look to state law for resolution of these issues.
An interesting aspect of joint authorship is the so- called "duality of rights." Under this concept, "each author obtains an undivided ownership in the whole of the joint work, including any portion thereof. Consequently, one joint author thereby obtains the right to use or license that portion of the joint work that was the sole creation of the other joint author." A co-owner of a copyright cannot be liable to another co-owner for infringement of the copyright. Rather, as set forth above, a co-author's remedy for non-payment of his share of royalties is an action for an accounting under state law.
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You may not know who Mike the Ironman is, but he went all the way to Kenting, Taiwan to enter the 'Ironman 70.3 Taiwan' International Challenge on November 3, 2012. Mike dedicated this race to raise funds for underprivileged children in Hong Kong, hoping to use this meaningful experience to demonstrate faith, hope and love.
A person needs a soldier's heart to endure a triathlon race, and Mike's determination was strong enough to finish the race in six hours – one full hour faster than his expected time! For Mike's first-timer in such long distance race, it was a major milestone for him. Mike's determination touched the many hearts of the people around him, and they helped Mike raise a total amount of over HK$70,000.
After the race on 1 December, Mike paid visits to underprivileged families and children at Sham Shui Po and delivered daily necessities to them in person. Having grown up in a grassroots family, he truly understands the hardships of low-income families. He hopes the children in these families look positively towards life, and continue to spread the love Mike has for them out to others who are in need in society.
Mike was delighted to have finished the race within his personal goal, and felt even more touched to see so many people supporting his cause. He wanted to thank his family, friends, triathlon teammates, and most of all the blessing from Jesus Lord for giving him to give meaning to his race – all for the needy low-income families in Hong Kong.


An otherworldly creature is descending from the snow covered peaks of the Ore Mountains to deliver bleak ambient sounds for the impending winter. Expect to be shrouded by winds as cold as the longest winter nights by the sonority echoing in the valleys of frost and ice.
Velvet Dreams' "Darkened Mysteria" will be released by INTO ENDLESS CHAOS RECORDS on the 24.09.2018 as Digipack limited to 100 copies so you better act fast!


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Harvard Gazette
Eradicating Malaria a Tall Order
HUCE faculty associates Barry Bloom, former dean of the Harvard School of Public Health and Marcia Castro, assistant professor of demography at HSPH joined Jeremy Greene, assistant professor of the history of science in the Faculty of Arts and Sciences and instructor in medicine at Harvard Medical School; Professor of Public Policy Amitabh Chandra of Harvard Kennedy School; Günther Fink, assistant professor of international health economics at HSPH; Amy Bei, a postdoctoral fellow at HSPH; and David Sengeh, a former Harvard undergraduate and current graduate student at Massachusetts Institute of Technology to discuss the difficult challenge of eradicating malaria.


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"…a very exciting prospect to both audiences and composers in England" – Sound and Music
Patricia Alessandrini: Esquisses pour alphabet de ténèbres
for flute, clarinet, piano, percussion, violin, cello and electronics
Pedro Alvarez: New forms of asymmetry
for solo guitar and ensemble
Diego Castro Magas, guitar
Martin Iddon: crinaeae
for flute, clarinet, piano, percussion, violin, cello
Mauricio Pauly: El teléfono en reposo pero atento
for amplified ensemble and live electronics
Performances and events
International Anthony Burgess Foundation Manchester
17 November 2015, 7:30pm
Clothworkers Centenary Concert Hall, University of Leeds
Goldsmiths University London, Deptford Town Hall Council Chambers
Ensemble-in-residence at Goldsmiths University London
Open rehearsal, Goldsmiths University London
20 November 2015, 10am–1:30pm
In November 2015 Ensemble Interface is heading to the United Kingdom for a touring project, featuring new pieces by Patricia Alessandrini, Pedro Alvarez, Martin Iddon and Mauricio Pauly, plus workshop activities in collaboration with UK universities.
One of the purposes of this project is to support the development and performance of pieces by UK-based composers, all the while encouraging the exchange between composers, ensemble and audience through various workshop formats. Performances will be accompanied by workshops for students and open panel discussions for the audience, purposing to deepen the interest and the understanding towards this repertoire.
The tour is meant to be the starting point of a long-term collaboration, including a recording project, cooperating with CD labels in Germany and England, featuring also collaborations with UK-based ensembles and broadening the scope to more composers from both countries.
The project has been granted support by the Sound and Music Touring Programme. Ensemble Interface achieved this award after entering into a competitive process with over 70 other applicants. In Sound and Music's view, Interface project "offers a very exciting prospect to both audiences and composers in England". The project is also supported by the Britten-Pears Foundation and the Goethe-Institut.
Alphabet de ténèbres(hommage à Ligeti) will allow the pianist of the Ensemble Interface to pilot the ensemble parts and live electronics by playing, in alternation, on a keyboard and a piano, using special touch-sensing technology. She will do this by playing an existing series of pieces, a selection of études by Ligeti (from Book #1), in a manner not entirely audible to the public, focussing on the instrumental gesture, thus be playing one piece in order to unfold an entirely different piece of music: my own composition, which is in turn based on various settings of the Lamentations of Jeremiah. Her tempo and expressivity (as measured by rubato and amplitude) will shape the expressivity of the instrumental parts. The communication between her performance and the instrumental parts will be obtained through some very simple programs I will install on each player's iPhone, Android, or other personal device (using PD and MobMuPlat, a special framework for smartphone programming). The devices will in turn provide audio and visual cues which will guide the performers through the score. This will be particularly important for the microtonal tunings, and for moments in which the performers use their voices.
A numerical system relating to the original scores of the Ligeti will relate to the Hebrew letters which begin each verse of the Lamentations. The piece will reference settings by Tallis, Couperin, Palestrina, Lasso, Victoria, Zelenka, and others. I hope in this manner to use abstract materials – the letters of the alphabet – to convey emotional states. _ Patricia Alessandrini
New Forms of Asymmetry – Neither descriptive nor programmatic, the title is rather a literary 'found object'; a serendipitous conceptual catalyst for a single episode in a larger creative quest that seeks to put form, rather than material, at the centre of aesthetic enquiries. The fragile relation between acoustic guitar and ensemble, not only in the deliberate instrumental writing but also in the different nature of their sound projection, facilitates the necessary reinvention of a 'concertante' dialectics. _ Pedro Alvarez
In Greek mythology, the crinaeae were the naiads, water nymphs, who protected and dwelled in fountains and wells. Sacred fountains and wells were intimately linked with inspiration in the Greek world: the Hippocrene fountain on Mount Helicon, formed by the hooves of Pegasus, was sacred to
the Muses, and the Pirene fountain in Corinth, where Pegasus himself would drink, was visited by Roman poets in search of inspiration. Other fountains, like those at Delphi, were fed by the Castilian Spring, and thus were intertwined with sites of prophecy. As Robin Lane-Fox has it, 'wherever there was water, there was a possible source of prophecy'.
Like numerous other of my pieces—most particularly hamadrayds (2010) and the other pieces named for the individual hamadryads, such as Ptelea (2014), all of which make use of readings of Josquin's Nymphes des bois in order to generate pitch material—crineae utilises a high Renaissance source in the background, here Dufay's isorhythmic motet, Salve flos tusce gentis, which concerns itself with the naiads. Highly active on the rhythmic level, but extremely quiet—always on the borders of silence—and with a broadly static set of pitch materials—deployed most often as points, extended lines, or brief glissandi—crinaeae is composed of individual part books, with no score, such that the fine details of the piece are highly determined, but points of entry and, indeed, form in a broader sense, occurs in the moment of performance.
crinaeae is a part of a larger cycle of pieces, each named for a particular type of naiad, begun with eleionomae (premiered by Distractfold in 2013), and will continue in limnades, potameides, and pegaeae. _ Martin Iddon
The new piece Patrulla palinodia by Mauricio Pauly will be the fifth in a series of works where amplification is actively controlled by the ensemble via stomp-boxes as an integrated form of articulation, resonance and live electronics. Extreme synchrony and virtuosic instrumental control coalesce to form a body of sound which shifts in mass and depth, falls apart and blends again, discovering its own shape as it moves forward.


A rose garden with a trellis on the estate of Albert C. Neufeld at 204 West Whitney.
The house was built by Mr. & Mrs. Neufeld in 1940-1941and was designed by Clarence Jahn of Foeller, Schober, Berners, Safford, & Jahn of Green Bay in the Georgian Revival style. Mr. Neufeld was in the lumber business and was a founding stockholder of Fort Howard Paper Company. The property was occupied by Mr. & Mrs. Neufeld until Mrs. Neufeld's death in 1975. Then their daughter, Mary Ellen, and her husband, Patrick Martin, occupied the property until her death in 2008. The property is currently owned by her children through the Mary Ellen Neufeld Martin Living Trust. The rose garden was on additional property purchased after World War II. It was laid out to serve as a location for garden parities. It was the location of the wedding reception for Mr. & Mrs. Neufeld's daughter in 1949 and their granddaughter in 1978. Jens Jensen was a consultant on the landscaping on the original property, making it one of the few Jens Jensen properties in Wisconsin. The house was featured in Architectural Digest in 1977. The interior has a quarter sawn white oak with raised paneling Library, a hanging black walnut spiral staircase, and antique Zuber wallpapered Dining Room. In the interior there is an 80 foot vista through the house from the Living Room fireplace through a bow window in the Dining Room that ended 180 feet later at the arbor in the center of the rose garden. Both Mr. & Mrs. Neufeld and Mrs. Martin employed a full time gardener to care for the property. Mrs. Martin was assisted by Schroeder Flowers of Allouez.


Veneto Centre for Research Diagnosis and Treatment of Food Allergies and Intolerances, Department of Pediatrics - Padua General University Hospital, was founded in 2005 with the Regional Law n.26 of November 26th, 2004. The Centre Head is Dr. M. Antonella Muraro.
Allergic disease affects over 30% of children and the number of children presenting with allergies appears to be increasing. There are many ways that allergic reactions can be expressed such as eczema, urticaria (hives), asthma, rhinitis, gastrointestinal upset (where food allergy is suspected), conjunctivitis, and in more severe cases anaphylaxis.
Food allergy is an immunological reaction towards normal foods. Food intolerance, on the other hand, is due to a lack of enzymes and / or pharmacological or toxic mechanisms.