A two-stage generative model (Graph CVAE + flow matching) learns topology-agnostic motion codes from a new 5k-topology dataset and retargets video motion to arbitrary unseen skeletons.
arXiv preprint arXiv:2405.11126 , year=
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MOCHI enhances noisy collaborative human-object interaction captures via grasp optimization followed by diffusion-based full-body refinement that incorporates interaction information into single-person motion priors.
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TopoCap: Learning Topology-Agnostic Motion Priors for Monocular Video-to-Animation
A two-stage generative model (Graph CVAE + flow matching) learns topology-agnostic motion codes from a new 5k-topology dataset and retargets video motion to arbitrary unseen skeletons.
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MOCHI: Motion Enhancement of Collaborative Human-object Interactions
MOCHI enhances noisy collaborative human-object interaction captures via grasp optimization followed by diffusion-based full-body refinement that incorporates interaction information into single-person motion priors.