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About this paper
Zhangsihao Yang, Mingyuan Zhou, Mengyi Shan, Bingbing Wen, Ziwei Xuan, Mitch Hill, Junjie Bai, Guo-Jun Qi and 1 more
OmniMotionGPT introduces the first comprehensive solution for text-driven animal motion generation by developing a cross-domain knowledge transfer framework and the AnimalML3D dataset. The model generates diverse and realistic animal motions from text prompts, including human-like actions, achieving an R-precision of 0.850 for top-1 matches on out-of-distribution prompts, significantly outperforming a baseline of 0.407.
Key insights
- Abundant human motion data can be effectively leveraged to address data scarcity in text-driven animal motion generation.
- Semantic understanding of actions can be separated from specific anatomical structures, enabling transfer between different species.
- The concept of "intersecting primal joints" provides a robust method to establish commonalities between anatomically disparate skeletons.
- A multi-domain joint training approach allows for optimal alignment and knowledge transfer between human and animal motion data.
Summary by alphaXiv.
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