Code repository for paper "Self-supervised Learning of Detailed 3D Face Reconstruction (TIP2020)"
- python 3.7
- tensorflow 1.15
- g++ 4.8 (other version might also work)
- tf_mesh_renderer
Please read install.sh for more details. It installs all packages required to run the codes, and compiles the c++ kernel of the differentiable renderer. Pay attention to finding the correct path to TF_INC and TF_LIB. The path specified in install.sh might not suit your case. If it does not work, please try to find them manually. You can also compile the codes using methods provided by tf_mesh_renderer.
We release the checkpoint files, resources, and some testing results for our model. As the training tfrecords are huge, we only provide one tfrecord for testing our codes. We will release the url in baidu drive in some days.
- Please download the training data train_data.zip into the repo root directory ./ and unzip them.
- Please download the checkpoints ckptxxx.zip into ./results/ and unzip them.
- Please download the resources.zip into the repo root directory ./ and unzip it.
- Please download the frozen_models.zip into ./preprocess/ and unzip it. They are face detection and landmark detection models used in proprocessing a face image.
- In run_train.sh, we provide commands for training the coarse / fine model.
- In run_test.sh, we provide commands for inference. Note that the batch_size should be 1.
If you use the codes provided here in your paper, please cite the following.
@article{chen2019self,
title={Self-supervised Learning of Detailed 3D Face Reconstruction},
author={Chen, Yajing and Wu, Fanzi and Wang, Zeyu and Song, Yibing and Ling, Yonggen and Bao, Linchao},
journal={IEEE Transactions on Image Processing},
year={2020}
}
Our codes use the differentiable renderer and vggface backbone provided in the following two repos. We thank them for providing the codes.
- tf_mesh_renderer: https://github.com/google/tf_mesh_renderer
- vgg face: https://github.com/ZZUTK/Tensorflow-VGG-face
If you have any questions regarding this work, please send emails to [email protected].
MIT License
Copyright (c) [2020] [Yajing Chen]
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