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README.md

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-license: mit
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+---
+license: mit
+---
+
+<h2 align="center"> <a href="https://arxiv.org/abs/2503.10624">ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness</a>
+</h2>
+
+<h2 align="center">
+🏆 ICCV 2025 Highlight Paper 🏆
+</h2>
+
+<h3 align="center">
+
+[![arXiv](https://img.shields.io/badge/Arxiv-2503.10624-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2503.10624)
+[![Home Page](https://img.shields.io/badge/Project-Website-green.svg)](https://boqian-li.github.io/ETCH/) 
+[![X](https://img.shields.io/badge/@Boqian%20Li-black?logo=X)](https://x.com/Boqian_Li_/status/1908467186122817642)
+[![youtube](https://img.shields.io/badge/Video-E33122?logo=Youtube)](https://youtu.be/8_3DdW0cZqM)
+
+[Boqian Li](https://boqian-li.github.io/), 
+[Haiwen Feng](https://havenfeng.github.io/), 
+[Zeyu Cai](https://github.com/zcai0612), 
+[Michael J. Black](https://ps.is.mpg.de/person/black), 
+[Yuliang Xiu](https://xiuyuliang.cn/) 
+</h3>
+
+
+![image](assets/teasor.png)
+This repository is the official implementation of ETCH, a novel body fitting pipeline that estimates cloth-to-body surface mapping through locally approximate SE(3) equivariance, encoding tightness as displacement vectors from the cloth to the underlying body.
+
+## News 🚩
+- [2025-08-04] We release the `All-in-One` model, which is trained on the `4D-Dress` dataset, `CAPE` dataset, and Generative dataset, totally 94501 samples. Please download the all-in-one model from [here](https://drive.google.com/drive/folders/14zGMkmC580VLNgeUBFtM6FP8QX415VAa?usp=sharing).
+
+- [2025-08-04] We release the code for `ETCH`, please feel free to have a try!
+
+## Overview
+<div align="center">
+<img src="assets/overview.gif" width="400" />
+</div>
+
+Our key novelty is modeling cloth-to-body SE(3)-equivariant tightness vectors for clothed humans, abbreviated as ETCH, which resembles ``etching'' from the outer clothing down to the inner body. 
+
+Following this outer-to-inner mapping, ETCH regresses sparse body markers, simplifying clothed human fitting into an inner-body marker fitting task.
+
+## Environment Setup ⚙️
+
+```bash
+conda env create -f environment.yml
+conda activate etch
+cd external
+git clone https://github.com/facebookresearch/theseus.git && cd theseus
+pip install -e .
+cd ../..
+```
+
+## Data Preparation 📃 
+0. please note that we placed data samples in the `datafolder` folder for convenience. 
+1. Generate Anchor Points with Tightness Vectors (for training)
+    ```bash
+    python scripts/generate_infopoints.py
+    ```
+
+2. Get splitted ids (pkl file)
+    ```bash
+    python scripts/get_splitted_ids_{datasetname}.py
+    ```
+3. For body_models, please download with [this link](https://drive.google.com/file/d/1JNFk4OGfDkgE9WdJb1D1zGaECix8XpKV/view?usp=sharing), and place it under the `datafolder/` folder.
+
+4. please note that before the above processes, there are some preprocessing steps on the original data: 
+
+    for `4D-Dress` dataset, we apply zero-translation `mesh.apply_translation(-translation)` to the original scan and the body model; 
+    
+    for `CAPE` dataset, we used the processed meshes extracted from [PTF](https://github.com/taconite/PTF), in which we noticed that the SMPL body meshes are marginally different from the original SMPL body meshes but more precise.
+
+## Dataset Organization 📂
+The dataset folder tree is like:  
+```bash
+datafolder/
+├── datasetfolder/
+│   ├── model/ # scans
+│   │   ├── id_0
+│   │   │   └── id_0.obj
+│   ├── smpl(h)/ # body models
+│   │   ├── id_0
+│   │   │   ├── info_id_0.npz
+│   │   │   └── mesh_smpl_id_0.obj # SMPL body mesh
+├── useful_data_datasetname/
+├── gt_datasetname_data/
+│   ├── npz/
+│   │   └── id_0.npz
+│   └── ply 
+│       └── id_0.ply
+```
+please refer to the `datafolder` folder for more details. 
+
+
+## Training 🚀
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train.py --batch_size 2 --i datasetname_settingname 
+# batch_size should <= num_data, if you just have the sample data, you can set batch_size to 1
+```
+
+## Evaluation 📊
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/eval.py --batch_size 3 --model_path path_to_pretrained_model --i datasetname_settingname
+
+# please note that the train_ids has no overlap with the val_ids, the sample data is from train_ids, so if you want to test the pretrained model on the sample data, you should set the activated_ids_path to the train_ids.pkl file for successful selection.
+```
+
+## Pretrained Model used in the paper 
+Please download the pretrained model used in the paper from [here](https://drive.google.com/drive/folders/14zGMkmC580VLNgeUBFtM6FP8QX415VAa?usp=sharing). 
+
+
+## 🔥 All-in-One Model 🔥
+We provide the `All-in-One` model, which is trained on the `4D-Dress` dataset, `CAPE` dataset, and Generative dataset, totally 94501 samples. Please download the all-in-one model from [here](https://drive.google.com/drive/folders/14zGMkmC580VLNgeUBFtM6FP8QX415VAa?usp=sharing).
+
+For demo inference, you can use the following command:
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/inference_demo.py --scan_path path_to_scan_obj_file --gender gender --model_path path_to_allinone_pretrained_model
+```
+
+Please note that during the training of `All-in-One` model and in the `inference_demo.py` file, we centering the scan as input, and re-center the predicted SMPL mesh to the original scan.
+For more details, please refer to the `src/inference_demo.py` file.
+
+We also provide the animation function, which can be used to animate the scan with the predicted SMPL mesh. please refer to the `src/animation.py` file for more details.
+
+## Citation
+
+```bibtex
+@inproceedings{li2025etch,
+  title     = {{ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness}},
+  author    = {Li, Boqian and Feng, Haiwen and Cai, Zeyu and Black, Michael J. and Xiu, Yuliang},
+  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+  year      = {2025}
+}
+```
+
+## Acknowledgments
+We thank [Marilyn Keller](https://marilynkeller.github.io/) for the help in Blender rendering, [Brent Yi](https://brentyi.github.io/) for fruitful discussions, [Ailing Zeng](https://ailingzeng.site/) and [Yiyu Zhuang](https://github.com/yiyuzhuang) for HuGe100K dataset, [Jingyi Wu](https://github.com/wjy0501) and [Xiaoben Li](https://xiaobenli00.github.io/) for their help during rebuttal and building this open-source project, and the members of [Endless AI Lab](http://endless.do/) for their help and discussions. This work is funded by the Research Center for Industries of the Future (RCIF) at Westlake University, the Westlake Education Foundation. [Yuliang Xiu](https://xiuyuliang.cn/) also received funding from the Max Planck Institute for Intelligent Systems.
+
+Here are some great resources we benefit from:
+
+- [ArtEq](https://github.com/HavenFeng/ArtEq) and [EPN_PointCloud](https://github.com/nintendops/EPN_PointCloud) for the Equivariant Point Network.
+- [theseus](https://github.com/facebookresearch/theseus) for the implementation of Levenberg–Marquardt algorithm.
+- [smplx](https://github.com/vchoutas/smplx) for the SMPL body model.
+- [point-transformer](https://github.com/POSTECH-CVLab/point-transformer) for the Point Transformer network.
+- [Robust Weight Transfer](https://github.com/rin-23/RobustSkinWeightsTransferCode) for SMPL-based animation.
+
+## Contributors
+Kudos to all of our amazing contributors! This open-source project is made possible by the contributions of the following individuals:
+
+<a href="https://github.com/boqian-li/ETCH/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=boqian-li/ETCH" />
+</a>
+
+## License
+ETCH is released under the [MIT License](LICENSE).
+
+## Disclosure
+While MJB is a co-founder and Chief Scientist at Meshcapade, his research in this project was performed solely at, and funded solely by, the Max Planck Society.
+
+
+## Contact
+For technical questions, please contact Boqian Li via [email protected].
+
+
+
+
+