Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics

• URL: http://arxiv.org/abs/2408.04631v2
• Date: Thu, 28 Aug 2025 01:30:18 GMT
• Title: Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics
• Authors: Ruining Li, Chuanxia Zheng, Christian Rupprecht, Andrea Vedaldi,
• Abstract summary: We introduce Puppet-Master, an interactive video generator that captures the internal, part-level motion of objects.<n>We demonstrate that Puppet-Master learns to generate part-level motions, unlike other motion-conditioned video generators.<n>Puppet-Master generalizes well to out-of-domain real images, outperforming existing methods on real-world benchmarks.
• Score: 79.4785166021062
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce Puppet-Master, an interactive video generator that captures the internal, part-level motion of objects, serving as a proxy for modeling object dynamics universally. Given an image of an object and a set of "drags" specifying the trajectory of a few points on the object, the model synthesizes a video where the object's parts move accordingly. To build Puppet-Master, we extend a pre-trained image-to-video generator to encode the input drags. We also propose all-to-first attention, an alternative to conventional spatial attention that mitigates artifacts caused by fine-tuning a video generator on out-of-domain data. The model is fine-tuned on Objaverse-Animation-HQ, a new dataset of curated part-level motion clips obtained by rendering synthetic 3D animations. Unlike real videos, these synthetic clips avoid confounding part-level motion with overall object and camera motion. We extensively filter sub-optimal animations and augment the synthetic renderings with meaningful drags that emphasize the internal dynamics of objects. We demonstrate that Puppet-Master learns to generate part-level motions, unlike other motion-conditioned video generators that primarily move the object as a whole. Moreover, Puppet-Master generalizes well to out-of-domain real images, outperforming existing methods on real-world benchmarks in a zero-shot manner.

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