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Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation

Code for our ECCV 2022 paper "Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation".

Paper | Presentation Video | Supplementary Video | Poster

Expression editing

Age editing

Wrinkle line editing

Getting started

• Clone this repository and cd facial-detail-manipulation

• Run conda create -n semm python=3.7 && conda activate semm

• Run pip install -r requirements.txt

• Install pytorch==1.9.0 torchvision==0.10.0 from PyTorch.

• Install pytorch3d==0.6.2 from PyTorch3D. The author uses pip install "git+https://github.com/facebookresearch/pytorch3d.git@v0.6.2".

• Install ibug-face-detection and ibug-face-alignment.

• Download pretrained model from here, unzip and put SEMM into ./checkpoints.

• Download model_mse.pth from here and put it into ./checkpoints.

• Download facescape_bilinear_model_v1_3.zip from https://facescape.nju.edu.cn/Page_Download/ and put core_847_50_52.npy into ./predef.

• Download dpmap_single_net_G.pth from here and put it into ./checkpoints.

• Install Blender 3.2 and FFmpeg. Make sure blender and ffmpeg can be accessed in shell, or you can set the blender_path and ffmpeg_path in ./experiments/both_cond_launcher.py.

• Install scipy for Blender. Typically, we should first download the get-pip script, run ${blender_path}/3.2/python/bin/python3.10 get-pip.py and then run ${blender_path}/3.2/python/bin/python3.10 -m pip install scipy.

Usage

Data preprocessing

Before performing the editing tasks below, we should first pre-process the data via

python -m detail_shape_process.detail_process --input ./demo/input --output ./demo/output

Expression and age editing

python -m experiments both_cond exp_age_edit SEMM --gpu_id 0

The reconstruction results are located in ./demo/output/${filename}/recon/, the expression editing results are in ./demo/output/${filename}/exp_edit/, and the age editing results are in ./demo/output/${filename}/age_edit/.

Expression interpolation

python -m experiments both_cond exp_inter SEMM --gpu_id 0

The results will be in ./demo/output/${filename}/exp_inter_${target_exp_name}/. You can set which data to process and the target expression via file_paths and target_exps in exp_inter_options in the file ./experiments/both_cond_launcher.py.

Age progression animation

python -m experiments both_cond age_progression SEMM --gpu_id 0

The results will be in ./demo/output/${filename}/age_progression/.

Blendshape animation

python -m experiments both_cond bs_anime SEMM --gpu_id 0

The results will be in ./demo/output/${filename}/bs_anime_${clip_name}/.

Interactive wrinkle line editing

python -m experiments both_cond demo SEMM --gpu_id 0

An GUI window will pop up. You can import a displacement map and edit it by drawing or erasing lines.

conda install -c anaconda py-opencv
conda install -c alges pyqt 

Training

We provide a small sample dataset that comprises identities in FaceScape's publishable_list_v1.txt. You can commence training by utilizing the sample dataset through the following command:

python -m experiments both_cond train SEMM --gpu_id 01

The default configuration assumes running on two RTX 3090 GPUs.

Training data preprocessing

Given that the sample dataset is insufficiently small to lead to favorable results, it is necessary to apply and download Topologically Uniformed Model(TU-Model) (facescape_trainset_001_100.zip ~ facescape_trainset_801_847.zip) in the FaceScape dataset.

Once downloaded, extract the dataset to /path/to/FaceScape. The folder should comprise of 847 folders, labeled from 1 to 847.

Next, use the following command to process the training dataset:

python -m detail_shape_process.train_detail_process --input /path/to/FaceScape --output /path/to/processed/dataset

Lastly, make sure to update the subsequent line:

        opt.set(
            dataroot="./predef/sample_dataset/",  # just a small sample dataset

in experiments/both_cond_launcher.py to point to the processed dataset.

Acknowledgements

The network to extract wrinkle lines is from sketch_simplification. The 3DMM fitting part is adapted from Detailed3DFace. We use the facial landmark detector in face_alignment. The training code is based on swapping-autoencoder-pytorch. The texture extractor code is from DECA. The drawing GUI is based on DeepFaceDrawing-Jittor. We thank the above projects for making their code open-source.

Citation

If you find our code or paper useful, please cite as:

@inproceedings{ling2022structure,
  title={Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation},
  author={Ling, Jingwang and Wang, Zhibo and Lu, Ming and Wang, Quan and Qian, Chen and Xu, Feng},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year = {2022}
}