DiBiHand: Diffusion Guided Bilateral Hand Composition

This paper proposes DiBiHand, a novel diffusion-based framework for 3D hand mesh recovery. In contrast to conventional discriminative pipelines that directly regress hand parameters from images via multi-stage approaches such as detect, crop, flip, box, and joint analysis, DiBiHand formulates hand pose estimation as a reverse diffusion process, progressively denoising random samples drawn from a Gaussian prior to produce realistic hand meshes. The method employs the MANO (hand Model with Articulated and Non-rigid defOrmations) parametric model to represent hand poses, supporting our proposed adaptive axis-angle (A-axis) and continuous 6D rotation representations (Rot-6D) for stable and efficient rotation learning. For bilateral hand pose estimation, the model handles both left and right hands simultaneously through a unified pose representation that concatenates rotations for all joints. Training with random masking simulates partial observations and occlusions, enhancing the robustness of the model under real-world conditions. The diffusion-based formulation inherently captures the complex, multi-modal nature of hand articulations and provides a strong generative prior that can serve as a regularizer for conditional refinement tasks. Meanwhile, DiBiHand achieves state-of-the-art performance on single and bi-hand benchmarks, e.g., 4.6 JPE on FreiHand, 4.5 JPE on DexYCB, and an average 64% [email protected] on HInt. The code will be available online.