HandDiffuse: Generative Controllers for Two-Hand Interactions via
  Diffusion Models
Pei Lin Sihang Xu Hongdi Yang Yiran Liu Xin Chen Jingya Wang Jingyi Yu Lan Xu
Existing hands datasets are largely short-range and the interaction is weak
due to the self-occlusion and self-similarity of hands, which can not yet fit
the need for interacting hands motion generation. To rescue the data scarcity,
we propose HandDiffuse12.5M, a novel dataset that consists of temporal
sequences with strong two-hand interactions. HandDiffuse12.5M has the largest
scale and richest interactions among the existing two-hand datasets. We further
present a strong baseline method HandDiffuse for the controllable motion
generation of interacting hands using various controllers. Specifically, we
apply the diffusion model as the backbone and design two motion representations
for different controllers. To reduce artifacts, we also propose Interaction
Loss which explicitly quantifies the dynamic interaction process. Our
HandDiffuse enables various applications with vivid two-hand interactions,
i.e., motion in-betweening and trajectory control. Experiments show that our
method outperforms the state-of-the-art techniques in motion generation and can
also contribute to data augmentation for other datasets. Our dataset,
corresponding codes, and pre-trained models will be disseminated to the
community for future research towards two-hand interaction modeling.