FaceScape provides large-scale high-quality 3D face datasets, parametric models, docs and toolkits about 3D face related technology. [CVPR2020 paper] [extended arXiv Report] [supplementary]
Our latest progress will be updated to this repository constantly - [latest update: 2023/6/27]
The data can be downloaded in https://facescape.nju.edu.cn/ after requesting a license key.
New: Share link on Google Drive is available after requesting license key, view here for detail.
New: The bilinear model ver1.6 can be downloaded without requesting a license key, view here for the link and rules.
The available sources include:
Item (Docs) | Description | Quantity | Quality |
---|---|---|---|
TU models | Topologically uniformed 3D face models with displacement map and texture map. |
16940 models (847 id × 20 exp) |
Detailed geometry, 4K dp/tex maps |
Multi-view data | Multi-view images, camera parameters and corresponding 3D face mesh. |
>400k images (359 id × 20 exp × ≈60 view) |
4M~12M pixels |
Bilinear model | The statistical model to transform the base shape into the vector space. |
4 for different settings | Only for base shape. |
Info list | Gender / age of the subjects. | 847 subjects | -- |
The datasets are only released for non-commercial research use. As facial data involves the privacy of participants, we use strict license terms to ensure that the dataset is not abused.
We present a benchmark to evaluate the accuracy of single-view face 3D reconstruction (SVFR) methods, view here for the details.
Start using python toolkit here, the demos include:
- bilinear_model-basic - use facescape bilinear model to generate 3D mesh models.
- bilinear_model-fit - fit the bilinear model to 2D/3D landmarks.
- multi-view-project - Project 3D models to multi-view images.
- landmark - extract landmarks using predefined vertex index.
- facial_mask - extract facial region from the full head TU-models.
- render - render TU-models to color images and depth map.
- alignment - align all the multi-view models.
- symmetry - get the correspondence of the vertices on TU-models from left side to right side.
- rig - rig 20 expressions to 52 expressions.
High-fidelity 3D Face Generation from Natural Language Descriptions (CVPR 2023)
Menghua Wu, Hao Zhu#, Linjia Huang, Yiyu Zhuang, Yuanxun Lu, Xun Cao
RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs (AAAI 2023)
Longwei Guo, Hao Zhu#, Yuanxun Lu, Menghua Wu, Xun Cao
Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation (ECCV2022)
Jingwang Ling, Zhibo Wang, Ming Lu, Quan Wang, Chen Qian, Feng Xu
HeadNeRF: A Real-Time NeRF-Based Parametric Head Model (CVPR2022)
Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, Juyong Zhang
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations (CVPR2022)
Mingwu Zheng, Hongyu Yang, Di Huang, Liming Chen
Detailed Facial Geometry Recovery from Multi-view Images by Learning an Implicit Function (AAAI 2022)
Yunze Xiao*, Hao Zhu*, Haotian Yang, Zhengyu Diao, Xiangju Lu, Xun Cao
Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment (ACM MM 2021)
Yuxing Wang, Yawen Lu, Zhihua Xie, Guoyu Lu
Detailed Riggable 3D face Prediction Code of FaceScape (CVPR2020)
Haotian Yang*, Hao Zhu*, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, Xun Cao
- 2022/9/9
One section is added to introduce open-source projects that uses FaceScape data or models, and will be continuously updated. - 2022/7/26
The data for training and testing MoFaNeRF is added to the download page. - 2021/12/2
Benchmark to evaluate single-view face reconstruction is available, view here for detail. - 2021/8/16
Share link on google drive is available after requesting license key, view here for detail. - 2021/5/13
Fitting demo is added to toolkit. Please note if you download bilinear model v1.6 before 2021/5/13, you need to download it again, because some parameters required by fitting demo are supplemented. - 2021/4/14
The bilinear model has been updated to 1.6, check it here.
The new bilinear model now can be downloaded from NJU drive or Google Drive without requesting a license key. Check it here.
ToolKit and Doc has been updated with new content.
Some wrong ages and genders in the info list are corrected in "info_list_v2.txt". - 2020/9/27
The code of detailed riggable 3D face prediction is released, check it here. - 2020/7/25
Multi-view data is available for download.
Bilinear model is updated to ver 1.3, with vertex-color added.
Info list including gender and age is available in download page.
Tools and samples are added to this repository. - 2020/7/7
Bilinear model is updated to ver 1.2. - 2020/6/13
The website of FaceScape is online.
3D models and bilinear models are available for download. - 2020/3/31
The pre-print paper is available on arXiv.
If you find this project helpful to your research, please consider citing:
@InProceedings{yang2020facescape,
author = {Yang, Haotian and Zhu, Hao and Wang, Yanru and Huang, Mingkai and Shen, Qiu and Yang, Ruigang and Cao, Xun},
title = {FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020},
page = {601--610}}
Exntended version with the benchmark:
@article{zhu2021facescape,
title={FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction},
author={Zhu, Hao and Yang, Haotian and Guo, Longwei and Zhang, Yidi and Wang, Yanru and Huang, Mingkai and Shen, Qiu and Yang, Ruigang and Cao, Xun},
journal={arXiv preprint arXiv:2111.01082},
year={2021}
}
The project is supported by CITE Lab of Nanjing University, Baidu Research, and Aiqiyi Inc. The student contributors: Ji Shengyu, Jin Wei, Huang Mingkai, Wang Yanru, Yang Haotian, Zhang Yidi, Xiao Yunze, Ding Yuxin, Guo Longwei.