关键词: 单视图人体重建, 人体尺寸, 人体测量, 遮挡人体, 分级比例约束, 迭代优化

Abstract: In real-world scenarios, human images are often affected by clothing, self-occlusion, and environmental object occlusion, leading to insufficient visible information. Existing human mesh recovery methods tend to degrade to average models in shape estimation, failing to faithfully recover individual geometric characteristics. To address this issue, Human Dimension Attention Regressor for monocular occluded human mesh recovery (HDAR) was proposed for occluded human mesh recovery. Firstly, the visible human regions were used to infer the dimensions of occluded parts. Secondly, a hierarchical proportion constraint mechanism was then introduced, in which first-level constraints were applied to adjacent human parts and second-level constraints were applied to more distant parts, ensuring that the regressed shapes conform to human anatomical structure. Finally, 2D joint information was integrated with the estimated human dimensions in an iterative optimization process to improve pose estimation accuracy. On the 3DPW (3D Poses in the Wild) dataset, the method achieves a per-vertex error (PVE) of 65.2 mm, which is 10.7 mm lower than that of Multi-HMR(Multi-Person Whole-Body Human Mesh Recovery) under occlusion conditions, corresponding to a 14.1% error reduction. Visualization experiments demonstrate that the method effectively improves the recovery accuracy of human shape and pose in complex occlusion scenarios.

Key words: Keywords: monocular human recovery, human dimension, human measurement, occluded human body, hierarchical proportion constraint, iterative optimization