By Haoran Bai, Di Kang, Haoxian Zhang, Jinshan Pan, and Linchao Bao
FFHQ-UV is a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions, which are desired characteristics for rendering realistic 3D face models under different lighting conditions.
The dataset is derived from a large-scale face image dataset namely FFHQ, with the help of our fully automatic and robust UV-texture production pipeline. Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches to generate multi-view normalized face images from single-image inputs. An elaborated UV-texture extraction, correction, and completion procedure is then applied to produce high-quality UV-maps from the normalized face images. Compared with existing UV-texture datasets, our dataset has more diverse and higher-quality texture maps.
[2022-01-19] The source codes are available.
[2022-12-16] The OneDrive download link is available.
[2022-12-16] The AWS CloudFront download link is offline.
[2022-12-06] The script for generating face images from latent codes is available.
[2022-12-02] The latent codes and attributes of the multi-view normalized face images are available.
[2022-12-02] The FFHQ-UV-Interpolate dataset is available.
[2022-12-01] The FFHQ-UV dataset is available.
[2022-11-28] The paper is available here.
- Linux + Anaconda
- CUDA 10.0 + CUDNN 7.6.0
- Python 3.7
- dlib:
pip install dlib
- PyTorch 1.7.1:
pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2
- TensorBoard:
pip install tensorboard
- TensorFlow 1.15.0:
pip install tensorflow-gpu==1.15.0
- MS Face API:
pip install --upgrade azure-cognitiveservices-vision-face
- Other packages:
pip install tqdm scikit-image opencv-python pillow imageio matplotlib mxnet Ninja google-auth google-auth-oauthlib click requests pyspng imageio-ffmpeg==0.4.3 scikit-learn torchdiffeq==0.0.1 flask kornia==0.2.0 lmdb psutil dominate rtree
- PyTorch3D and Nvdiffrast:
mkdir thirdparty
cd thirdparty
git clone https://github.com/facebookresearch/iopath
git clone https://github.com/facebookresearch/fvcore
git clone https://github.com/facebookresearch/pytorch3d
git clone https://github.com/NVlabs/nvdiffrast
conda install -c bottler nvidiacub
pip install -e iopath
pip install -e fvcore
pip install -e pytorch3d
pip install -e nvdiffrast
- Baidu Netdisk: download link (extract code: 5wbi).
- OneDrive: download link
|--FFHQ-UV
|--ffhq-uv # FFHQ-UV dataset
|--ffhq-uv-face-latents # The normalized face images' latent codes of FFHQ-UV dataset
|--ffhq-uv-face-attributes # The normalized face images' attributes of FFHQ-UV dataset
|--ffhq-uv-interpolate # FFHQ-UV-Interpolate dataset
|--ffhq-uv-interpolate-face-latents # The normalized face images' latent codes of FFHQ-UV-Interpolate dataset
|--ffhq-uv-interpolate-face-attributes # The normalized face images' attributes of FFHQ-UV-Interpolate dataset
- FFHQ-UV-Interpolate is a variant of FFHQ-UV. Please refer to this readme for details.
- We provide the latent codes of the multi-view normalized face images which are used for extracting texture UV-maps. Along with the latent codes, we also provide the attributes (gender, age, beard) of each face, which are detected by Microsoft Face API.
- One can generate face images from download latent codes by using the following script.
sh run_gen_face_from_latent.sh # Please refer to this script for detailed configuration
- Please refer to this readme for details of checkpoints.
- Please refer to this readme for details of topology assets.
- Prepare a directory of dataset project, which contains a "images" subfolder.
- Put the original images into the "images" subfolder.
- Modify the configuration and then run the following script to create the facial UV-texture dataset.
sh run_ffhq_uv_dataset.sh # Please refer to this script for detailed configuration
- Put the input images into a folder.
- Modify the configuration and then run the following script for fitting.
run_rgb_fitting.sh # Please refer to this script for detailed configuration
@article{FFHQ-UV,
title={FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction},
author={Bai, Haoran and Kang, Di and Zhang, Haoxian and Pan, Jinshan and Bao, Linchao},
journal={arXiv preprint arXiv:2211.13874},
year={2022}
}