上海科技大学知识管理系统

The recently emerging text -to -motion advances have inspired 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 released to the community for future research, which can be found at github.