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arXiv · 2312.07937

BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics

Wenqian Zhang·Molin Huang·Yuxuan Zhou·Juze Zhang·Jingyi Yu·Jingya Wang·Lan Xu
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Abstract

The recently emerging text-to-motion advances have spired numerous attempts for convenient and interactive human motion generation. Yet, existing methods are largely limited to generating body motions only without considering the rich two-hand motions, let alone handling various conditions like body dynamics or texts. To break the data bottleneck, we propose BOTH57M, a novel multi-modal dataset for two-hand motion generation. Our dataset includes accurate motion tracking for the human body and hands and provides pair-wised finger-level hand annotations and body descriptions. We further provide a strong baseline method, BOTH2Hands, for the novel task: generating vivid two-hand motions from both implicit body dynamics and explicit text prompts. We first warm up two parallel body-to-hand and text-to-hand diffusion models and then utilize the cross-attention transformer for motion blending. Extensive experiments and cross-validations demonstrate the effectiveness of our approach and dataset for generating convincing two-hand motions from the hybrid body-and-textual conditions. Our dataset and code will be disseminated to the community for future research.

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Wenqian Zhang, Molin Huang, Yuxuan Zhou, Juze Zhang, Jingyi Yu, Jingya Wang, Lan Xu. 2023-12-13. BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics. https://doi.org/10.1109/cvpr52733.2024.00232

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