Enabling Visual Composition and Animation in Unsupervised Video Generation
<|reference_start|>Enabling Visual Composition and Animation in Unsupervised Video Generation: In this work we propose a novel method for unsupervised controllable video generation. Once trained on a dataset of unannotated videos, at inference our model is capable of both composing scenes of predefined object parts and animating them in a plausible and controlled way. This is achieved by conditioning video generation on a randomly selected subset of local pre-trained self-supervised features during training. We call our model CAGE for visual Composition and Animation for video GEneration. We conduct a series of experiments to demonstrate capabilities of CAGE in various settings. Project website: https://araachie.github.io/cage.<|reference_end|>
@article{davtyan2024enabling,
    title={Enabling Visual Composition and Animation in Unsupervised Video
  Generation},
    author={Aram Davtyan, Sepehr Sameni, Bj"orn Ommer, Paolo Favaro},
    journal={arXiv preprint arXiv:2403.14368},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14368},
    primaryClass={cs.CV}
}