A Versatile and Differentiable Hand-Object Interaction Representation

Synthesizing accurate hands-object interactions (HOI) is critical for applications in Computer Vision, Augmented Reality (AR), and Mixed Reality (MR). Despite recent ad-vances, the accuracy of reconstructed or generated HOI leaves room for refinement. Some techniques have improved the accuracy of dense correspondences by shifting focus from generating explicit contacts to using rich HOI fields. Still, they lack full differentiability or continuity and are tai-lored to specific tasks. In contrast, we present a Coarse Hand-Object Interaction Representation (CHOIR), a novel, versatile and fully differentiable field for HOI modelling. CHOIR leverages discrete unsigned distances for continu-ous shape and pose encoding, alongside multivariate Gaus-sian distributions to represent dense contact maps with few parameters. To demonstrate the versatility of CHOIR we design JointDiffus ion, a diffusion model to learn a grasp distribution conditioned on noisy hand-object interactions or only object geometries, for both refinement and synthe-sis applications. We demonstrate JointDiffus ion, s improve-ments over the SOTA in both applications: it increases the contact F1 score by 5% for refinement and decreases the sim. displacement by 46% for synthesis. Our exper-iments show that JointDiffusion with CHOIR yield supe-rior contact accuracy and physical realism compared to SOTA methods designed for specific tasks. Project page: https://theomorales.com/CHOIR