Improve model card: Add pipeline tag, GitHub link, abstract, and sample usage

README.md

@@ -5,14 +5,29 @@ tags:
 - 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)</p>
+#### <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, 
+``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
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