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Context: Intuitively, the key to video generation is to keep the continuous motion of consistent objects.So we make the following observations on state-of-the-art T2I diffusion models [31] that motivate our method accordingly.(1) Regarding motion: T2I models are able to generate images that align well with the text, including the verb terms. For example, given the text prompt “a man is running on the beach”, the T2I models produce the snapshot where a man is running (not walking or jumping), but not continuously (the first row of Fig. 2).This serves as evidence that T2I models can properly attend to verbs via cross-modal attention for static motion generation.(2) Regarding consistent objects: Simply extending the self-attention in the T2I model from one image to multiple images maintains content consistency across frames.Taking the same example, when we generate consecutive frames in parallel with extended cross-frame attention to the 1st frame, the same man and the same beach can be observed in the resultant sequence though the motion is still not continuous (the second row of Fig. 2). This implies that the self-attention layers in T2I models are only driven by spatial similarities rather than pixel positions. We implement our findings into a simple yet effective method, dubbed as Tune-A-Video, for the task of One-Shot Video Generation.Tune-A-Video is based on a simple inflation of pre-trained T2I diffusion models [31] over time dimension, by extending the 3\times 3 convolution to 1\times 3\times 3, and spatial self-attention to spatio-temporal cross-frame attention.We propose an efficient tuning strategy that only updates the projection matrices in attention blocks to capture continuous motion dynamics from the one-shot video, with the rest of parameters being frozen. To imitate how humans adapt new visual concepts from a single exemplar, we introduce a new T2V generation task called One-Shot Video Generation, where only a single text-video pair is provided for training an open-domain T2V generator.To this end, we propose Tune-A-Video, a simple yet effective solution based on pretrained T2I diffusion models.Our method exploits the properties of pretrained T2I models by extending spatial self-attention to spatio-temporal domain with a tailored Sparse-Causal Attention.To capture necessary motion knowledge, we update the projection matrices in attention block on one training sample. Given this, our Tune-A-Video supports several T2V applications, including subject replacement, background change, attribute modification, style transfer, and etc. More recently, Imagen Video [14] improves VDM with cascaded diffusion models and v-prediction parameterization to generate high definition videos. Phenaki [41] is the first work to generate videos from time variable prompts. To achieve this, they compresses videos to small representations of discrete tokens with causal attention in time, and thus can handle variable-length videos. To address the lack of video-text pair data, they joint train on a large scale of image-text pairs and a smaller number of video-text pairs, achieving better generalization results than available video datasets. Make-A-Video [36] shares similar motivation and aims to transfer the significant progress in T2I generation to T2V generation. They combine the appearance-text information from text-image data together with the world movements from unsupervised video footage, and achieve the state-of-the-art in T2V generation.We follow [36] to use pre-trained T2I diffusion models and propose Tune-A-Video for one-shot T2V generation. Differently, Tune-A-Video explores a more efficient tuning strategy that only updates the projection matrices in attention blocks to capture continuous motion changes.

Question: Is it inspired by transformer network?
The authors extend the spatial self-attention in the T2I model from one image to multiple images to maintain content consistency across frames. It is useful in spatiotemporal domain like generating videos.