AnimateZoo: Zero-shot Video Generation of Cross-Species Animation via Subject Alignment
<|reference_start|>AnimateZoo: Zero-shot Video Generation of Cross-Species Animation via Subject Alignment: Recent video editing advancements rely on accurate pose sequences to animate subjects. However, these efforts are not suitable for cross-species animation due to pose misalignment between species (for example, the poses of a cat differs greatly from that of a pig due to differences in body structure). In this paper, we present AnimateZoo, a zero-shot diffusion-based video generator to address this challenging cross-species animation issue, aiming to accurately produce animal animations while preserving the background. The key technique used in our AnimateZoo is subject alignment, which includes two steps. First, we improve appearance feature extraction by integrating a Laplacian detail booster and a prompt-tuning identity extractor. These components are specifically designed to capture essential appearance information, including identity and fine details. Second, we align shape features and address conflicts from differing subjects by introducing a scale-information remover. This ensures accurate cross-species animation. Moreover, we introduce two high-quality animal video datasets featuring a wide variety of species. Trained on these extensive datasets, our model is capable of generating videos characterized by accurate movements, consistent appearance, and high-fidelity frames, without the need for the pre-inference fine-tuning that prior arts required. Extensive experiments showcase the outstanding performance of our method in cross-species action following tasks, demonstrating exceptional shape adaptation capability. The project page is available at https://justinxu0.github.io/AnimateZoo/.<|reference_end|>
@article{xu2024animatezoo:,
    title={AnimateZoo: Zero-shot Video Generation of Cross-Species Animation via
  Subject Alignment},
    author={Yuanfeng Xu, Yuhao Chen, Zhongzhan Huang, Zijian He, Guangrun Wang,
  Philip Torr, Liang Lin},
    journal={arXiv preprint arXiv:2404.04946},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04946},
    primaryClass={cs.CV}
}