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; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 2393-2404

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 which can be found at https://github.com/Godheritage/BOTH2Hands.