用于生成3D人脸的卷积网格自动编码器_Python_Shell_下载.zip

# CoMA: Convolutional Mesh Autoencoders  This is an official repository of [Generating 3D Faces using Convolutional Mesh Autoencoders](https://coma.is.tue.mpg.de) [[Project Page](https://coma.is.tue.mpg.de)][[Arxiv](https://arxiv.org/abs/1807.10267)] **UPDATE :** Thank you for using and supporting this repository over the last two years. This will no longer be maintained. Alternatively, please use: - [sw-gong/coma](https://github.com/sw-gong/coma), thanks to Shunwang Gong. - [pixelite1201/pytorch_coma](https://github.com/pixelite1201/pytorch_coma/), thanks to Priyanka Patel. ## Requirements This code is tested on Tensorflow 1.3. Requirements (including tensorflow) can be installed using: ```bash pip install -r requirements.txt ``` Install mesh processing libraries from [MPI-IS/mesh](https://github.com/MPI-IS/mesh). ## Data Download the data from the [Project Page](https://coma.is.tue.mpg.de). Preprocess the data ```bash python processData.py --data <PATH_OF_RAW_DATA> --save_path <PATH_TO_SAVE_PROCESSED DATA> ``` Data pre-processing creates numpy files for the interpolation experiment and extrapolation experiment (Section X of the paper). This creates 13 different train and test files. `sliced_[train|test]` is for the interpolation experiment. `<EXPRESSION>_[train|test]` are for cross validation cross 12 different expression sequences. ## Training To train, specify a name, and choose a particular train test split. For example, ```bash python main.py --data data/sliced --name sliced ``` ## Testing To test, specify a name, and data. For example, ```bash python main.py --data data/sliced --name sliced --mode test ``` #### Reproducing results in the paper Run the following script. The models are slightly better (~1% on average) than ones reported in the paper. ```bash sh generateErrors.sh ``` ## Sampling To sample faces from the latent space, specify a model and data. For example, ```bash python main.py --data data/sliced --name sliced --mode latent ``` A face template pops up. You can then use the keys `qwertyui` to sample faces by moving forward in each of the 8 latent dimensions. Use `asdfghjk` to move backward in the latent space. For more flexible usage, refer to [lib/visualize_latent_space.py](https://github.com/anuragranj/coma/blob/master/lib/visualize_latent_space.py). ## Acknowledgements We thank [Raffi Enficiaud](https://www.is.mpg.de/person/renficiaud) and [Ahmed Osman](https://ps.is.tuebingen.mpg.de/person/aosman) for pushing the release of [psbody.mesh](https://github.com/MPI-IS/mesh), an essential dependency for this project. ## License The code contained in this repository is under MIT License and is free for commercial and non-commercial purposes. The dependencies, in particular, [MPI-IS/mesh](https://github.com/MPI-IS/mesh) and our [data](https://coma.is.tue.mpg.de) have their own license terms which can be found on their respective webpages. The dependencies and data are NOT covered by MIT License associated with this repository. ## Related projects [CAPE (CVPR 2020)](https://github.com/QianliM/CAPE): Based on CoMA, we build a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making a generative, animatable model of people in clothing. A large-scale mesh dataset of clothed humans in motion is also included! ## When using this code, please cite Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. "Generating 3D faces using Convolutional Mesh Autoencoders." European Conference on Computer Vision (ECCV) 2018.