Ponimator: Unfolding Interactive Pose for Versatile Human-human
  Interaction Animation
Close-proximity human-human interactive poses convey rich contextual
information about interaction dynamics. Given such poses, humans can
intuitively infer the context and anticipate possible past and future dynamics,
drawing on strong priors of human behavior. Inspired by this observation, we
propose Ponimator, a simple framework anchored on proximal interactive poses
for versatile interaction animation. Our training data consists of
close-contact two-person poses and their surrounding temporal context from
motion-capture interaction datasets. Leveraging interactive pose priors,
Ponimator employs two conditional diffusion models: (1) a pose animator that
uses the temporal prior to generate dynamic motion sequences from interactive
poses, and (2) a pose generator that applies the spatial prior to synthesize
interactive poses from a single pose, text, or both when interactive poses are
unavailable. Collectively, Ponimator supports diverse tasks, including
image-based interaction animation, reaction animation, and text-to-interaction
synthesis, facilitating the transfer of interaction knowledge from high-quality
mocap data to open-world scenarios. Empirical experiments across diverse
datasets and applications demonstrate the universality of the pose prior and
the effectiveness and robustness of our framework.
68f1c7cb6e0bef323a68febb
Shaowei Liu
68f1c7cb6e0bef323a68febc
Chuan Guo
68f1c7cb6e0bef323a68febd
Bing Zhou
68f1c7cb6e0bef323a68febe
Jian Wang
Ponimator: Unfolding Interactive Pose for Versatile Human-human
  Interaction Animation
Close-proximity human-human interactive poses convey rich contextual
information about interaction dynamics. Given such poses, humans can
intuitively infer the context and anticipate possible past and future dynamics,
drawing on strong priors of human behavior. Inspired by this observation, we
propose Ponimator, a simple framework anchored on proximal interactive poses
for versatile interaction animation. Our training data consists of
close-contact two-person poses and their surrounding temporal context from
motion-capture interaction datasets. Leveraging interactive pose priors,
Ponimator employs two conditional diffusion models: (1) a pose animator that
uses the temporal prior to generate dynamic motion sequences from interactive
poses, and (2) a pose generator that applies the spatial prior to synthesize
interactive poses from a single pose, text, or both when interactive poses are
unavailable. Collectively, Ponimator supports diverse tasks, including
image-based interaction animation, reaction animation, and text-to-interaction
synthesis, facilitating the transfer of interaction knowledge from high-quality
mocap data to open-world scenarios. Empirical experiments across diverse
datasets and applications demonstrate the universality of the pose prior and
the effectiveness and robustness of our framework.
Ponimator uses conditional diffusion models to generate and synthesize interactive poses from motion capture data, enabling versatile interaction animation tasks.
conditional diffusion models pose animator pose generator interactive pose priors image-based interaction animation reaction animation text-to-interaction synthesis motion capture data