FaceScape

FaceScape provides large-scale high-quality 3D face datasets, parametric models, docs and toolkits about 3D face related technology. [CVPR2020 paper] [supplemetary]

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	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 paramters and coresponding 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.

Docs

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.

Code

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

ChangeLog

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}}

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.

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