Cascaded Diffusion Framework for Probabilistic Coarse-to-Fine Hand Pose Estimation

Abstract

Deterministic models for 3D hand pose reconstruction, whether single-staged or cascaded, struggle with pose ambiguities caused by self-occlusions and complex hand articulations. Existing cascaded approaches progressively refine pose predictions in a coarse-to-fine manner, but their deterministic nature prevents them from modeling pose uncertainty. Conversely, recent probabilistic methods capture pose distributions but are confined to single-stage estimation, often yielding inaccurate 3D reconstructions without refinement. To address these limitations, we propose a coarse-to-fine cascaded diffusion framework that combines probabilistic modeling with cascaded refinement. The first stage is a joint diffusion model that samples diverse 3D joint hypotheses, and the second stage is a Mesh Latent Diffusion Model (Mesh LDM) that reconstructs a 3D hand mesh conditioned on a joint sample. Our key idea is to use these diverse hypotheses as a training signal rather than as final outputs, so that the Mesh LDM learns distribution-aware joint-mesh relationships and becomes robust to the variation in coarse predictions. Extensive ablations validate the necessity of the cascaded design and the choice of latent-space diffusion. Experiments on FreiHAND, HO3Dv2, and DexYCB show that our method achieves state-of-the-art performance and remains stable under occlusion and pose ambiguity.