Bring Your Own Characters: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters

This repository is the official implementation of Bring Your Own Characters published in IEEE-VR 2024.

Introduction

• BYOC is a holistic solution for creating facial animations of virtual human in VR applications. This solution incorporates two key contributions:
  • (1) a deep learning model that transforms human facial images into desired blendshape coefficients and replicate the specified facial expression.
  • (2) a Unity-based toolkit that encapsulates the deep learning model, allowing users to utilize the trained model to create facial animation when developing their own VR applications.
• We also conduct a user study, collecting feedback from a wide range of potential users. It would inspire more future works in this direction.

Setup

Environment

git clone https://github.com/showlab/BYOC
cd BYOC
conda create -n byoc python=3.8
conda activate byoc
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt

Download Models and Files

Pre-traind Base Model

• Go to google drive to download the pre-trained model.
• Put the downloaded file at ./image2bs/landmark2exp/checkpoints/facerecon..

Dependent Data

• Go to google drive to download the essential files.
• Unzip and put the BFM folder at ./image2bs/landmark2exp.

File Structure

• training folder is a training workspace for you to train the Adapter model on your customized dataset.
• image2bs folder is an inference workspace for you to debug the full inference pipeline of Base Model + Adapter Model, and test the performance of your trained model.
• BlendshapeToolkit is a project of Unity that implements the toolkit with rich user interface.

Training

Prepare Dataset

Option 1: Try the training dataset provided by us

1. Go to google drive to download the training dataset.
2. Put blendshape_gt.json at ./training/Deep3DFaceRecon_pytorch/datasets
3. Put test_images at ./training/Deep3DFaceRecon_pytorch/checkpoints/facerecon/results/

Option 2: Prepare the training dataset by yourself

1. Run the script ./training/construct_dataset/random_blendshape_weights.py to generate customized dataset.
2. Put the generated ./training/construct_dataset/blendshape_gt/xxxx.csv into MAYA Software to generate training images.
3. Put the generated training images into ./image2bs/test_images and run the script ./image2bs/process_training_dataset.py
4. Put ./training/construct_dataset/blendshape_gt.json into ./training/Deep3DFaceRecon_pytorch/datasets
5. Put ./image2bs/landmark2exp/checkpoints/facerecon/results/test_images into ./training/Deep3DFaceRecon_pytorch/checkpoints/facerecon/results/

Start Training!

1. Run bash train.sh to train the model.
2. Run the command python -m visdom.server -port 8097 to start visualization of training.

Inference (Python)

1. Put the pretrained_model latest_net_V.pth into ./image2bs/exp2bs/checkpoint/EXP2BSMIX/Exp2BlendshapeMixRegress.
2. Put your video into ./image2bs/test_video or put images into ./image2bs/test_images.
3. Run the script inference.py to inference only.

Usage (Unity)

1. In the image2bs/inference.bat file, (1) change e: on the second line to the disk partition name where image2bs is located, such as d:. (2) Change the path following cd on the third line to the absolute path of the image2bs folder. (3) Change PYTHON_PATH to the absolute path of the folder containing the Python interpreter used for inference. (4) Change INF_PATH to the absolute path of the image2bs folder.
2. Start the BlendshapeToolkit with Unity and run the project.
3. Click the Initialize button to call image2bs project to generate animation. For more details about usage of the toolkit, please refer to our paper.

Acknowledgement

We are grateful for the following awesome projects:

• Deep3DFaceRecon_pytorch
• pix2pix