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

• URL: http://arxiv.org/abs/2408.04631v1
• Date: Thu, 8 Aug 2024 17:59:38 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 present Puppet-Master, an interactive video generative model that can serve as a motion prior for part-level dynamics. At test time, given a single image and a sparse set of motion trajectories, Puppet-Master can synthesize a video depicting realistic part-level motion faithful to the given drag interactions.
• Score: 67.97235923372035
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present Puppet-Master, an interactive video generative model that can serve as a motion prior for part-level dynamics. At test time, given a single image and a sparse set of motion trajectories (i.e., drags), Puppet-Master can synthesize a video depicting realistic part-level motion faithful to the given drag interactions. This is achieved by fine-tuning a large-scale pre-trained video diffusion model, for which we propose a new conditioning architecture to inject the dragging control effectively. More importantly, we introduce the all-to-first attention mechanism, a drop-in replacement for the widely adopted spatial attention modules, which significantly improves generation quality by addressing the appearance and background issues in existing models. Unlike other motion-conditioned video generators that are trained on in-the-wild videos and mostly move an entire object, Puppet-Master is learned from Objaverse-Animation-HQ, a new dataset of curated part-level motion clips. We propose a strategy to automatically filter out sub-optimal animations and augment the synthetic renderings with meaningful motion trajectories. Puppet-Master generalizes well to real images across various categories and outperforms existing methods in a zero-shot manner on a real-world benchmark. See our project page for more results: vgg-puppetmaster.github.io.

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