| license: mit | |
| tags: | |
| - smpl | |
| - human-pose-and-shape-estimation | |
| - human-mesh-recovery | |
| - inverse-kinematics | |
| pipeline_tag: keypoint-detection | |
| # Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose Inverse Kinematics | |
| #### <p align="center">[arXiv Paper](https://arxiv.org/abs/2508.13562) | [Code](https://github.com/Charrrrrlie/Learnable-SMPLify)</p> | |
| ## 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. | |
| --- | |
| ``TL;DR`` Given X_{t-s} and X_{t} 3D keypoints, | |
| calculate residual SMPL parameters from t-s to t. | |
| ## Sample Usage | |
| To run sequential inference using the trained model, navigate to the cloned repository and execute the following command: | |
| ```bash | |
| python inference.py <PATH_TO_CHECKPOINT> (<DATASET_NAME> <SAMPLE_RATIO>) | |
| ``` | |
| For detailed installation and data preparation, as well as instructions for training and evaluation, please refer to the [GitHub repository](https://github.com/Charrrrrlie/Learnable-SMPLify). | |
| ## Citation | |
| If you find this work useful in your research, please consider citing: | |
| ``` | |
| @misc{LearnableSMPLify, | |
| title={Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose Inverse Kinematics}, | |
| author={Yuchen, Yang and Linfeng, Dong and Wei, Wang and Zhihang, Zhong and Xiao, Sun}, | |
| year={2025}, | |
| eprint={2508.13562}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` |