Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose Inverse Kinematics

• URL: http://arxiv.org/abs/2508.13562v1
• Date: Tue, 19 Aug 2025 06:53:57 GMT
• Title: Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose Inverse Kinematics
• Authors: Yuchen Yang, Linfeng Dong, Wei Wang, Zhihang Zhong, Xiao Sun,
• Abstract summary: Learnable SMPLify is a neural framework that replaces the iterative fitting process in SMPLify with a single-pass regression model.<n>It achieves nearly 200x faster runtime compared to SMPLify, generalizes well to unseen 3DPW and RICH, and operates as a model-agnostic manner when used as a plug-in tool on LucidAction.
• Score: 13.621560002904873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In 3D human pose and shape estimation, SMPLify remains a robust baseline that solves inverse kinematics (IK) through iterative optimization. However, its high computational cost limits its practicality. Recent advances across domains have shown that replacing iterative optimization with data-driven neural networks can achieve significant runtime improvements without sacrificing accuracy. Motivated by this trend, we propose Learnable SMPLify, a neural framework that replaces the iterative fitting process in SMPLify with a single-pass regression model. The design of our framework targets two core challenges in neural IK: data construction and generalization. To enable effective training, we propose a temporal sampling strategy that constructs initialization-target pairs from sequential frames. To improve generalization across diverse motions and unseen poses, we propose a human-centric normalization scheme and residual learning to narrow the solution space. Learnable SMPLify supports both sequential inference and plug-in post-processing to refine existing image-based estimators. Extensive experiments demonstrate that our method establishes itself as a practical and simple baseline: it achieves nearly 200x faster runtime compared to SMPLify, generalizes well to unseen 3DPW and RICH, and operates in a model-agnostic manner when used as a plug-in tool on LucidAction. The code is available at https://github.com/Charrrrrlie/Learnable-SMPLify.

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