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.

提出了用于生成双手动作的新型多模态数据集BOTH57M，包括准确的人体和手部动作跟踪、手指级别的手部标注和身体描述。使用两个并行的身体到手部和文本到手部扩散模型，通过交叉注意力变换器实现动作融合，从混合的身体和文本条件中生成令人信服的双手动作。将数据集和源代码提供给社区进行未来研究。

BOTH2Hands: 从文本提示和身体动力学推断三维手部姿态