Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics
<|reference_start|>Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics: 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.<|reference_end|>
@article{li2024puppet-master:,
    title={Puppet-Master: Scaling Interactive Video Generation as a Motion Prior
  for Part-Level Dynamics},
    author={Ruining Li, Chuanxia Zheng, Christian Rupprecht, Andrea Vedaldi},
    journal={arXiv preprint arXiv:2408.04631},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04631},
    primaryClass={cs.CV cs.AI}
}