Toward accurate hand mesh estimation via masked image modeling

Introduction With an enormous number of hand images generated over time, leveraging unlabeled images for pose estimation is an emerging yet challenging topic. While some semi-supervised and self-supervised methods have emerged, they are constrained by their reliance on high-quality keypoint detection models or complicated network architectures. Methods We propose a novel selfsupervised pretraining strategy for 3D hand mesh regression. Our approach integrates a multi-granularity strategy with pseudo-keypoint alignment in a teacher–student framework, employing self-distillation and masked image modeling for comprehensive representation learning. We pair this with a robust pose estimation baseline, combining a standard vision transformer backbone with a pyramidal mesh alignment feedback head. Results Extensive experiments demonstrate HandMIM’s competitive performance across diverse datasets, notably achieving an 8.00 mm Procrustes alignment vertex-point-error on the challenging HO3Dv2 test set, which features severe hand occlusions, surpassing many specially optimized architectures.