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。