A Twist Representation and Shape Refinement Method for Human Mesh Recovery

3D human mesh recovery from single RGB images or monocular videos is a challenging task. The twist representation utilized in existing inverse kinematics-based methods fails to accurately describe the twisting posture when the estimated bone direction is imprecise. Additionally, supervising SMPL shape parameters has the issue of shape estimation overfitting due to limited training data. This often results in compromised bone lengths that subsequently impair the precision of joint positions. To address these issues, we propose a framework that breaks down both human pose and shape into finer components, effectively managing and minimizing errors within each component. The proposed framework integrates two key advancements: the advanced Ortho-Twist and Swing Representation (OTSR) and the Skeleton-Focused Shape Refinement (SFSR). OTSR offers a more sophisticated representation for limb rotations compared to the traditional twist angle and swing representation to enhance the accuracy of twisting posture estimation. SFSR refines the estimated SMPL shape parameters by fitting bone lengths using the estimated joint positions, thereby significantly mitigating shape overfitting and enhancing joint position accuracy in the recovered mesh. We conduct experiments on the Human3.6 M and 3DPW datasets. The results demonstrate the superiority of the proposed framework in both single-image and video scenarios. Additionally, the ablation studies confirm the effectiveness of our proposed modules, and further generalizability experiments demonstrate that our two key advancements can serve as plug-and-play modules to enhance existing methods.