BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics
This work addresses a gap in human motion generation for interactive applications by enabling hand motion synthesis from hybrid conditions, though it is incremental as it builds on existing text-to-motion methods.
The paper tackled the problem of generating realistic two-hand motions from both body dynamics and text prompts, proposing a new dataset and baseline method that achieved effective results in cross-validations.
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.