Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456 Paper page - Ponimator: Unfolding Interactive Pose for Versatile Human-human
Interaction Animation
https://github.com/stevenlsw/ponimator\n","updatedAt":"2025-10-17T04:51:42.861Z","author":{"_id":"670462c6fd5ef6902568d7bd","avatarUrl":"/avatars/2d6ea275ccc289f6f1b07d0c8e860888.svg","fullname":"Shaowei Liu","name":"shaoweiliu","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":2,"identifiedLanguage":{"language":"en","probability":0.3569193184375763},"editors":["shaoweiliu"],"editorAvatarUrls":["/avatars/2d6ea275ccc289f6f1b07d0c8e860888.svg"],"reactions":[],"isReport":false}},{"id":"68f2ee80be30c71e25b9ba9f","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false},"createdAt":"2025-10-18T01:33:52.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Text2Interact: High-Fidelity and Diverse Text-to-Two-Person Interaction Generation](https://huggingface.co/papers/2510.06504) (2025)\n* [MoReact: Generating Reactive Motion from Textual Descriptions](https://huggingface.co/papers/2509.23911) (2025)\n* [InterPose: Learning to Generate Human-Object Interactions from Large-Scale Web Videos](https://huggingface.co/papers/2509.00767) (2025)\n* [InfinityHuman: Towards Long-Term Audio-Driven Human](https://huggingface.co/papers/2508.20210) (2025)\n* [MoSA: Motion-Coherent Human Video Generation via Structure-Appearance Decoupling](https://huggingface.co/papers/2508.17404) (2025)\n* [VividAnimator: An End-to-End Audio and Pose-driven Half-Body Human Animation Framework](https://huggingface.co/papers/2510.10269) (2025)\n* [PersonaAnimator: Personalized Motion Transfer from Unconstrained Videos](https://huggingface.co/papers/2508.19895) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"
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Ponimator uses conditional diffusion models to generate and synthesize interactive poses from motion capture data, enabling versatile interaction animation tasks.
AI-generated summary
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.
We propose Ponimator (ICCV 2025), a generative framework that turns interactive poses into realistic human–human motion, supporting image, text, and pose-based interaction animation.