DisContact: Generating Hand Motion and Discrete Contact Maps with Geometry-Coupled Hybrid Diffusion

Generating hand-object interactions requires modeling two tightly coupled but structurally different quantities: continuous articulated hand motion and discrete contact over the hand surface. Existing approaches place both in a shared continuous or latent space, which ignores this difference. We introduce DisContact, a hybrid diffusion framework that generates each modality in its native state space and couples them through the geometry they share. Hand motion follows a Gaussian DDPM, while per-vertex binary contact follows a Bernoulli D3PM whose stationary distribution is the empirical contact frequency of each vertex. At every denoising step, the current motion estimate is decoded through differentiable MANO kinematics into vertex-aligned geometry that conditions contact denoising, and pooled contact features refine the motion in return. A structured denoiser further exploits mesh connectivity and anatomical hand regions. On HoloAssist and ARCTIC, DisContact improves contact F1 and motion accuracy over prior work in nearly all settings, raising contact F1 on ARCTIC from 0.41 to 0.59. Controlled comparisons show that most of the benefit of coupling comes from explicit geometry rather than generic feature exchange, and new object-free diagnostics show that the generated motion and contact are more consistent with each other. These results indicate that keeping each modality in its own generative process, while linking them through the geometry they describe, is an effective way to generate structured hand–object interactions.