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<p>We present SMPL-IKS, a mixed analytical-neural inverse kinematics solver that operates on the well-known Skinned Multi-Person Linear model (SMPL) to recover human mesh from 3D skeleton. The key challenges in the task are threefold: (1) Shape Mismatching, (2) Error Accumulation, and (3) Rotation Ambiguity. Unlike previous methods that rely on costly vertex up-sampling or iterative optimization, SMPL-IKS directly regresses the SMPL parameters (<i>i.e.</i>, shape and pose parameters) in a clean and efficient way. Specifically, we propose to infer <i>skeleton-to-mesh</i> via three explicit mappings viz. <i>Shape Inverse (SI)</i>, <i>Inverse kinematics (IK)</i>, and <i>Pose Refinement (PR)</i>. SI maps bone length to shape parameters, IK maps bone direction to pose parameters, and PR addresses errors accumulated along the kinematic tree. SMPL-IKS is general and thus extensible to MANO or SMPL-H models. Extensive experiments are conducted on various benchmarks for body-only, hand-only, and body-hand scenarios. Our model surpasses state-of-the-art methods by a large margin while being much more efficient. Data and code are available at <a href="https://github.com/Z-Z-J/SMPL-IKS">https://github.com/Z-Z-J/SMPL-IKS</a>.</p>

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SMPL-IKS: A Mixed Analytical-Neural Inverse Kinematics Solver for 3D Human Mesh Recovery

  • 期刊论文

  • 发表日期：

    2025年8月30日

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  • Zijian Zhang,
  • Muqing Wu,
  • Honghao Qi,
  • Tianyi Ma,
  • Min Zhao

摘要

We present SMPL-IKS, a mixed analytical-neural inverse kinematics solver that operates on the well-known Skinned Multi-Person Linear model (SMPL) to recover human mesh from 3D skeleton. The key challenges in the task are threefold: (1) Shape Mismatching, (2) Error Accumulation, and (3) Rotation Ambiguity. Unlike previous methods that rely on costly vertex up-sampling or iterative optimization, SMPL-IKS directly regresses the SMPL parameters (i.e., shape and pose parameters) in a clean and efficient way. Specifically, we propose to infer skeleton-to-mesh via three explicit mappings viz. Shape Inverse (SI), Inverse kinematics (IK), and Pose Refinement (PR). SI maps bone length to shape parameters, IK maps bone direction to pose parameters, and PR addresses errors accumulated along the kinematic tree. SMPL-IKS is general and thus extensible to MANO or SMPL-H models. Extensive experiments are conducted on various benchmarks for body-only, hand-only, and body-hand scenarios. Our model surpasses state-of-the-art methods by a large margin while being much more efficient. Data and code are available at https://github.com/Z-Z-J/SMPL-IKS.