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| <p align="center"> | |
| <h1 align="center">ECON: Explicit Clothed humans Optimized via Normal integration</h1> | |
| <p align="center"> | |
| <a href="http://xiuyuliang.cn/"><strong>Yuliang Xiu</strong></a> | |
| 路 | |
| <a href="https://ps.is.tuebingen.mpg.de/person/jyang"><strong>Jinlong Yang</strong></a> | |
| 路 | |
| <a href="https://hoshino042.github.io/homepage/"><strong>Xu Cao</strong></a> | |
| 路 | |
| <a href="https://ps.is.mpg.de/~dtzionas"><strong>Dimitrios Tzionas</strong></a> | |
| 路 | |
| <a href="https://ps.is.tuebingen.mpg.de/person/black"><strong>Michael J. Black</strong></a> | |
| </p> | |
| <h2 align="center">CVPR 2023 (Highlight)</h2> | |
| <div align="center"> | |
| <img src="./assets/teaser.gif" alt="Logo" width="100%"> | |
| </div> | |
| <p align="center"> | |
| <br> | |
| <a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a> | |
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| <a href='https://colab.research.google.com/drive/1YRgwoRCZIrSB2e7auEWFyG10Xzjbrbno?usp=sharing'><img src='https://colab.research.google.com/assets/colab-badge.svg' alt='Google Colab'></a> | |
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| <a href='https://carlosedubarreto.gumroad.com/l/CEB_ECON'><img src='https://img.shields.io/badge/Blender-F6DDCC.svg?logo=Blender' alt='Blender'></a> | |
| <br></br> | |
| <a href="https://arxiv.org/abs/2212.07422"> | |
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| </a> | |
| <a href='https://xiuyuliang.cn/econ/'> | |
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| </p> | |
| <br/> | |
| ECON is designed for "Human digitization from a color image", which combines the best properties of implicit and explicit representations, to infer high-fidelity 3D clothed humans from in-the-wild images, even with **loose clothing** or in **challenging poses**. ECON also supports **multi-person reconstruction** and **SMPL-X based animation**. | |
| <br/> | |
| <br/> | |
| ## News :triangular_flag_on_post: | |
| - [2023/02/27] ECON got accepted by CVPR 2023 as Highlight (top 10%)! | |
| - [2023/01/12] [Carlos Barreto](https://twitter.com/carlosedubarret/status/1613252471035494403) creates a Blender Addon ([Download](https://carlosedubarreto.gumroad.com/l/CEB_ECON), [Tutorial](https://youtu.be/sbWZbTf6ZYk)). | |
| - [2023/01/08] [Teddy Huang](https://github.com/Teddy12155555) creates [install-with-docker](docs/installation-docker.md) for ECON . | |
| - [2023/01/06] [Justin John](https://github.com/justinjohn0306) and [Carlos Barreto](https://github.com/carlosedubarreto) creates [install-on-windows](docs/installation-windows.md) for ECON . | |
| - [2022/12/22] <a href='https://colab.research.google.com/drive/1YRgwoRCZIrSB2e7auEWFyG10Xzjbrbno?usp=sharing' style='padding-left: 0.5rem;'><img src='https://colab.research.google.com/assets/colab-badge.svg' alt='Google Colab'></a> is now available, created by [Aron Arzoomand](https://github.com/AroArz). | |
| - [2022/12/15] Both <a href="#demo">demo</a> and <a href="https://arxiv.org/abs/2212.07422">arXiv</a> are available. | |
| ## TODO | |
| - [ ] Blender add-on for FBX export | |
| - [ ] Full RGB texture generation | |
| ## Key idea: d-BiNI | |
| d-BiNI jointly optimizes front-back 2.5D surfaces such that: (1) high-frequency surface details agree with normal maps, (2) low-frequency surface variations, including discontinuities, align with SMPL-X surfaces, and (3) front-back 2.5D surface silhouettes are coherent with each other. | |
| |Front-view|Back-view|Side-view| | |
| |:--:|:--:|:---:| | |
| ||||| | |
| <details><summary>Please consider cite <strong>BiNI</strong> if it also helps on your project</summary> | |
| ```bibtex | |
| @inproceedings{cao2022bilateral, | |
| title={Bilateral normal integration}, | |
| author={Cao, Xu and Santo, Hiroaki and Shi, Boxin and Okura, Fumio and Matsushita, Yasuyuki}, | |
| booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part I}, | |
| pages={552--567}, | |
| year={2022}, | |
| organization={Springer} | |
| } | |
| ``` | |
| </details> | |
| <br> | |
| <!-- TABLE OF CONTENTS --> | |
| <details open="open" style='padding: 10px; border-radius:5px 30px 30px 5px; border-style: solid; border-width: 1px;'> | |
| <summary>Table of Contents</summary> | |
| <ol> | |
| <li> | |
| <a href="#instructions">Instructions</a> | |
| </li> | |
| <li> | |
| <a href="#demo">Demo</a> | |
| </li> | |
| <li> | |
| <a href="#applications">Applications</a> | |
| </li> | |
| <li> | |
| <a href="#citation">Citation</a> | |
| </li> | |
| </ol> | |
| </details> | |
| <br/> | |
| ## Instructions | |
| - See [installion doc for Docker](docs/installation-docker.md) to run a docker container with pre-built image for ECON demo | |
| - See [installion doc for Windows](docs/installation-windows.md) to install all the required packages and setup the models on _Windows_ | |
| - See [installion doc for Ubuntu](docs/installation-ubuntu.md) to install all the required packages and setup the models on _Ubuntu_ | |
| - See [magic tricks](docs/tricks.md) to know a few technical tricks to further improve and accelerate ECON | |
| - See [testing](docs/testing.md) to prepare the testing data and evaluate ECON | |
| ## Demo | |
| ```bash | |
| # For single-person image-based reconstruction (w/ l visualization steps, 1.8min) | |
| python -m apps.infer -cfg ./configs/econ.yaml -in_dir ./examples -out_dir ./results | |
| # For multi-person image-based reconstruction (see config/econ.yaml) | |
| python -m apps.infer -cfg ./configs/econ.yaml -in_dir ./examples -out_dir ./results -multi | |
| # To generate the demo video of reconstruction results | |
| python -m apps.multi_render -n <filename> | |
| # To animate the reconstruction with SMPL-X pose parameters | |
| python -m apps.avatarizer -n <filename> | |
| ``` | |
| <br/> | |
| ## More Qualitative Results | |
| |  | | |
| | :------------------------------------: | | |
| | _Challenging Poses_ | | |
| |  | | |
| | _Loose Clothes_ | | |
| ## Applications | |
| |  |  | | |
| | :----------------------------------------------------------------------------------------------------: | :-----------------------------------------: | | |
| | _ECON could provide pseudo 3D GT for [SHHQ Dataset](https://github.com/stylegan-human/StyleGAN-Human)_ | _ECON supports multi-person reconstruction_ | | |
| <br/> | |
| <br/> | |
| ## Citation | |
| ```bibtex | |
| @inproceedings{xiu2023econ, | |
| title = {{ECON: Explicit Clothed humans Optimized via Normal integration}}, | |
| author = {Xiu, Yuliang and Yang, Jinlong and Cao, Xu and Tzionas, Dimitrios and Black, Michael J.}, | |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| month = {June}, | |
| year = {2023}, | |
| } | |
| ``` | |
| <br/> | |
| ## Acknowledgments | |
| We thank [Lea Hering](https://is.mpg.de/person/lhering) and [Radek Dan臎膷ek](https://is.mpg.de/person/rdanecek) for proof reading, [Yao Feng](https://ps.is.mpg.de/person/yfeng), [Haven Feng](https://is.mpg.de/person/hfeng), and [Weiyang Liu](https://wyliu.com/) for their feedback and discussions, [Tsvetelina Alexiadis](https://ps.is.mpg.de/person/talexiadis) for her help with the AMT perceptual study. | |
| Here are some great resources we benefit from: | |
| - [ICON](https://github.com/YuliangXiu/ICON) for SMPL-X Body Fitting | |
| - [BiNI](https://github.com/hoshino042/bilateral_normal_integration) for Bilateral Normal Integration | |
| - [MonoPortDataset](https://github.com/Project-Splinter/MonoPortDataset) for Data Processing, [MonoPort](https://github.com/Project-Splinter/MonoPort) for fast implicit surface query | |
| - [rembg](https://github.com/danielgatis/rembg) for Human Segmentation | |
| - [MediaPipe](https://google.github.io/mediapipe/getting_started/python.html) for full-body landmark estimation | |
| - [PyTorch-NICP](https://github.com/wuhaozhe/pytorch-nicp) for non-rigid registration | |
| - [smplx](https://github.com/vchoutas/smplx), [PyMAF-X](https://www.liuyebin.com/pymaf-x/), [PIXIE](https://github.com/YadiraF/PIXIE) for Human Pose & Shape Estimation | |
| - [CAPE](https://github.com/qianlim/CAPE) and [THuman](https://github.com/ZhengZerong/DeepHuman/tree/master/THUmanDataset) for Dataset | |
| - [PyTorch3D](https://github.com/facebookresearch/pytorch3d) for Differential Rendering | |
| Some images used in the qualitative examples come from [pinterest.com](https://www.pinterest.com/). | |
| This project has received funding from the European Union鈥檚 Horizon 2020 research and innovation programme under the Marie Sk艂odowska-Curie grant agreement No.860768 ([CLIPE Project](https://www.clipe-itn.eu)). | |
| ## Contributors | |
| Kudos to all of our amazing contributors! ECON thrives through open-source. In that spirit, we welcome all kinds of contributions from the community. | |
| <a href="https://github.com/yuliangxiu/ECON/graphs/contributors"> | |
| <img src="https://contrib.rocks/image?repo=yuliangxiu/ECON" /> | |
| </a> | |
| _Contributor avatars are randomly shuffled._ | |
| --- | |
| <br> | |
| ## License | |
| This code and model are available for non-commercial scientific research purposes as defined in the [LICENSE](LICENSE) file. By downloading and using the code and model you agree to the terms in the [LICENSE](LICENSE). | |
| ## Disclosure | |
| MJB has received research gift funds from Adobe, Intel, Nvidia, Meta/Facebook, and Amazon. MJB has financial interests in Amazon, Datagen Technologies, and Meshcapade GmbH. While MJB is a part-time employee of Meshcapade, his research was performed solely at, and funded solely by, the Max Planck Society. | |
| ## Contact | |
| For technical questions, please contact yuliang.xiu@tue.mpg.de | |
| For commercial licensing, please contact ps-licensing@tue.mpg.de | |