FaceScape

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: 2021/8/16]

Data

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.

Benchmark for SVFR

We present a benchmark to evaluate the accuracy of single-view face 3D reconstruction (SVFR) methods, view here for the details.

ToolKit

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.

Code

The code of detailed riggable 3D face prediction in our paper is released here.

ChangeLog

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.

Bibtex

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}
}

Acknowledge

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.

human2b/facescape