This is the submission for the REACT2024 competition from AIS lab, Ritsumeikan University, Japan.

Content List

• Team Members
• Introduction
• Results
  • Output comparison with Learning2Listen's baseline:
• Prequisites
• Installation
• Download checkpoints, external and dataset
• Run the demo
• Model input and output
• Dataloader
• Training
  • Command
• Metrics harvesting
  • Command
• Visualization
• Data
• Known issues
• Acknowledgements

Team Members

• Dam Quang Tien
• Nguyen Tri Tung Nguyen
• Tran Dinh Tuan
• Lee Joo-Ho

Introduction

We propose a novel approach to solve the problem of the REACT2024 competition in Online Appropriate Facial Reaction Generation. Our approach is based on the combination of the following techniques:

1. Finite Scalar Quantization (FSQ) allows better codebook usability, making a better face tokenization method and enabling us to create a single-frame face tokenization method.
2. Cross-modality attention (FIBER) allows better feature fusion between the speaker's voice and facial expression, and fusion between the speaker's feature and the listener's past feature. This allows it to generate consistent but dynamic reactions.

Results

The model's performance on the validation set is as follows:

	Appropriateness		Diversity			Realism	Synchrony
	FRC	FRD	FRDvs	FRVar	FRDiv	FRRea	FRSyn
Ground truth	8.35	0	0.2117	0.0635	0	-	48.42
Mime ~ speaker lagged	0.37	84.78	0.2117	0.0635	0.2483	-	43.50
Random	0.05	228.33	0.1666	0.0833	0.1666	-	46.27
Trans-VAE online	0.07	90.31	0.0064	0.0012	0.0009	69.19	44.65
BeLFusion(k=10)+BinarizedAUs	0.12	94.09	0.0379	0.0248	0.0397	-	49
Ours	0.26	86.73	0.1160	0.0346	0.1162	81.28	46.96

And on the test set, it is as follows:

	Appropriateness		Diversity			Realism	Synchrony
	FRC	FRD	FRDvs	FRVar	FRDiv	FRRea	FRSyn
Ours	0.3190	82.0288	0.1165	0.0344	0.1162	34.6685	43.0918

Qualitative

comparison_with_learning2listen.mp4

Prequisites

The code is developed and tested only with Python 3.10 in Ubuntu 22.04.

Installation

This project is developed with a dependency on PyTorch, PyTorch3D, lightning, and Transformers. The installation is as follows:

To visualize the output please have ffmpeg installed in your system.

1. Clone the repository.

2. Create a conda virtual environment and activate the environment:

   • conda create -n aisreact2024 python=3.10
   • conda activate aisreact2024

3. Install the CUDA in conda environment, this is for rebuild pytorch-3d, if your pytorch-3d is already installed, you can skip this step. The command is as follows: conda install cuda -c nvidia

4. Install other dependencies from requirements.txt:

   • pip install -r requirements.txt

5. Restart the terminal and activate the conda environment again.

6. Install PyTorch-3D with CUDA:

   • FORCE_CUDA=1 pip install "git+https://github.com/facebookresearch/pytorch3d.git@8772fe0de8b9332be501bf0e61bd5502e3075e60"

We noticed that the installation of PyTorch-3D with CUDA is not straightforward, sometimes it will cause an error, please follow the Pytorch-3D installation guide to solve the problem.

Download checkpoints, external and dataset

1. Download the checkpoints and external tool and put it to the checkpoint and external folder separately.
2. Please refer to the REACT2024 to download the dataset.
3. You can run the demo without the dataset.

Run the demo

We provide a simple live-demo for this model. The demo will take the speaker's voice and face as input and generate the listener's face and emotion as output. The demo will use the pre-trained model in the checkpoint folder to generate the output.

bash run_demo.bash

Model input and output

1. Input:

   • Speaker's voice: path to the audio file as the wav2vec will process the feature.

   • Speaker's face: a tensor 58D containing 3DMM features.

2. Output:

   • A tensor 58D containing 3DMM features of the listener's face.

   • A tensor 25D emotion features of the listener's face.

Dataloader

The dataloader is the same as the original dataloader, except that instead of loading MFCC as audio features, we change it to load the path to the audio file with the start and end time of the audio segment. This is because we use wav2vec2 to process the audio feature.

Training

Training is divided into 2 stages:

1. Train the tokenizer by running p1_face_tokenizer_pretrain.py. Change the config in the main function for the model config parameters and training hyperparameters.
2. Train the reaction predictor by running p2_reaction_predictor.py. Change the config in the main function for the model config parameters and training hyperparameters. Make sure that the path to the desired tokenizer is correct and its config is the same as its training one, or it will cause a loading error.

This code tracks the model version and config by pushing all hyperparameters to wandb. You can switch to TensorBoard by commenting out logging to wandb and adding TensorBoard instead.

In case of resuming training, you can specify the resume_checkpoint in the main function of the training script and set the argument resume to True.

Command

Please change the path to the dataset and place the checkpoint in the correct path before running the command.

1. Face Tokenizer training, after training it will save the checkpoint in the output_dir:

python p1_face_tokenizer.py --run_name "face_tokenizer" --quantize_type "fsq" --output_dir "output/face_tokenizer" --dataset_path "/home/usr/react2024/data"

2. Reaction Predictor training, after training it will save the checkpoint in the output_dir:

python p2_react_predictor --output_dir output --tokenizer_checkpoint checkpoint/face_tokenizer_epoch=185-step=37200.ckpt --dataset_path /home/usr/react2024/data

To see the training commands, please refer to the run.bash file.

Metrics harvesting

After each training section, it will collect predictions and compute the metrics.

You can turn off training by specifying test=True in the main parameter and input the test_checkpoint to run the model on the test/validation set by setting the collect_metrics_in to test or val in the p2_reaction_predictor.py file.

The script will visualize the predictions first, then run multiple test_dataloaders to collect the metrics for diversity. Note that if the data config submit_video_only is True, then the script will only render the 4 necessary submission videos. And if submit_video_only is False, then the script will render every render_skip_step in predictor_config.

You can control test output by modify the test in the end of the main function of p2_reaction_predictor.py.

Command

A sample command to run the test is as follows, please change the path to the dataset and place the checkpoint in the correct path before running the command:

python p2_react_predictor.py --test --test_checkpoint checkpoint/react_predictor_epoch=91-step=9200.ckpt --tokenizer_checkpoint checkpoint/face_tokenizer_epoch=185-step=37200.ckpt --output_dir output --dataset_path /home/usr/react2024/data

To see the test command, please check the run.bash file.

Visualization

The visualization code is contain in test_step function of p2_reaction_predictor.py. There will be folders of frame generated for the video and the video itself. The video will be generated by the ffmpeg command inside. So that, when run the test function it will automatic render the validation require video. The visualization folder is structured as

output_dir
├── run_name                # this is to denote training time
    ├── infer_name          # this is to denote the inference time
        ├── val
            ├── only_submit_video   
                ├── mesh_video_b*_ind* 
                # contain the frame of the mesh predicted
                    ├── frame_0.jpg
                    ├── frame_1.jpg
                    ├── ....
                    ├── frame_750.jpg
                ├── fake_video_b*_ind* 
                # contain the frame of the photo-rendered predicted
                ├── b*_ind*.json 
                # contain the address of frames for each video, can be used by `render_final_video.py` to render the video
                ├── b*_ind*.mp4 
                # video of speaker, listener, mesh, and photo-rendered from left to right  
            ├── render_for_fid    
                ├── mesh_video_b*_ind* 
                # contain the frame of the mesh predicted
                ├── fake_video_b*_ind* 
                # contain the frame of the photo-rendered predicted
                ├── b*_ind*.json 
                # contain the address of frames for each video, can be used by `render_final_video.py` to render the video
                ├── listener_gt.pt  
                # the ground truth listener's emotion vector
                ├── speaker_gt.pt   
                # the ground truth speaker's emotion vector
                ├── listener_pred.pt 
                # the predicted listener's emotion vector
                ├── metrics.txt 
                # the computed-metrics for the dataset
        ├── test            

Data

We have kept the data folder structure the same as the react2024 dataset. data/partition/modality/site/chat_index/person_index/clip_index/actual_data_files

The example of data structure.

data
├── test
├── val
├── train
   ├── Video_files
       ├── NoXI
           ├── 010_2016-03-25_Paris
               ├── Expert_video
               ├── Novice_video
                   ├── 1
                       ├── 1.jpg
                       ├── ....
                       ├── 751.jpg
                   ├── ....
           ├── ....
       ├── RECOLA
       ├── UDIVA
   ├── Audio_files
       ├── NoXI
       ├── RECOLA
           ├── group-1
               ├── P25 
               ├── P26
                   ├── 1.wav
                   ├── ....
           ├── group-2
           ├── group-3
   ├── Emotion
       ├── NoXI
       ├── RECOLA
           ├── group-1
               ├── P25 
               ├── P26
                   ├── 1.csv
                   ├── ....
           ├── group-2
           ├── group-3
   ├── 3D_FV_files
       ├── NoXI
       ├── RECOLA
           ├── group-1
               ├── P25 
               ├── P26
                   ├── 1.npy
                   ├── ....
           ├── group-2
           ├── group-3
            

• The task is to predict one role's reaction ('Expert' or 'Novice', 'P25' or 'P26'....) to the other ('Novice' or 'Expert', 'P26' or 'P25'....).
• 3D_FV_files involve extracted 3DMM coefficients (including expression (52 dim), angle (3 dim) and translation (3 dim) coefficients).
• The frame rate of processed videos in each site is 25 (fps = 25), height = 256, width = 256. And each video clip has 751 frames (about 30s), The samping rate of audio files is 44100.
• The csv files for baseline training and validation dataloader are now avaliable at 'data/train.csv' and 'data/val.csv'

Known issues

• Not yet fixed: scale_dot_product_attention might produce NaN when encountering an all-false mask on old CUDA devices, which can lead to test failures.

Acknowledgements

Thank @learning2listen, Lucidrains, and @nano-gpt for their open-source projects. Many parts of this project were originally forked from their repositories.