| title | MoZoo:Unleashing Video Diffusion power in animal fur and muscle simulation |
|---|---|
| authors | Dongxia Liu, Jie Ma, Xiaochen Yang, Jiancheng Zhang, Bin Xia, Zhehan Kan, Nisha Huang, Jun Liang, Wenming Yang, Jin Li |
| date | 2026-04-08 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.13857 |
| affiliation | Orange Team |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | The creation of cinematic-quality animal effects necessitates the precise modeling of muscle and fur dynamics, a process that remains both labor-intensive and computationally expensive within traditional production workflows. While generative diffusion models have shown promise in diverse artistic workflows, their capacity for high-fidelity animal simulation remains largely unexploited. We present MoZoo, a generative dynamics solver that bypasses conventional refinement to synthesize high-fidelity animal videos from coarse meshes under multimodal guidance. We propose Role-Aware RoPE (RAR-RoPE) which employs role-based index remapping to synchronize motion alignment while decoupling reference information via fixed temporal offsets. Complementing this, Asymmetric Decoupled Attention partitions the latent sequence to enforce a unidirectional information flow, effectively preventing feature interference and improving computational efficiency. To address the scarcity of high-quality training data, we introduce MoZoo-Data, a synthetic-to-real pipeline that leverages a rendering engine and an inverse mapping approach to construct a large-scale dataset of paired sequences. Furthermore, we establish MoZooBench, a comprehensive benchmark with 120 mesh-video pairs. Experimental results demonstrate that MoZoo achieves high-fidelity fur simulation across diverse animal skeletons and layouts, preserving superior temporal and structural consistency. |
cite
@misc{liu2026mozoo,
title={MoZoo:Unleashing Video Diffusion power in animal fur and muscle simulation},
author={Dongxia Liu and Jie Ma and Xiaochen Yang and Jiancheng Zhang and Bin Xia and Zhehan Kan and Nisha Huang and Jun Liang and Wenming Yang and Jin Li},
year={2026},
eprint={2605.13857},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.13857}
}