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MMFace4D: A Large-Scale Multi-Modal 4D Face Dataset for Audio-Driven 3D Face Animation

Haozhe Wu, Jia Jia, Junliang Xing, Hongwei Xu, Xiangyuan Wang, Jelo Wang [Paper]

plot

This repo gives the official code of the paper MMFace4D. The source code of 4D reconstruction, mesh sequence compression, and face animation baseline is given.

Notice that the reconstructed mesh sequences still have noise, and the fusion of multiview camera data is not good enough, we welcome researchers to provide a better version of 3D reconstruction.

MMFace4D Dataset Structure

We give the camera intrinsics, facial landmarks, speech audio, depth sequence, and 3D reconstructed sequence in the MMFace4D dataset. Each part is organized as follows:

Camera Intrinsics: The camera intrinsics give the intrinsic matrix of each camera, which has the same format as the API of Azure Kinect. The intrinsics matrices of each sequence are saved.

Facial Landmarks: We detect 2D facial landmarks of each video with HRNetv2. The landmarks of each video are saved as pickle format, which has shape (frame_num * 98 * 3).

Speech Audio: The speech audio is saved as wav format. Notice that for one recording, we have three videos from three perspectives, but we only have one speech audio.

Sentence Emotion: Neutral sentence has prefix 00, happy sentence has prefix 21, angry sentence has prefix 22, sad sentence has prefix 23, surprise sentence has prefix 24, fear sentence has prefix 25, disgust sentence has prefix 26.

Depth Sequence: the depth video is compressed as nut format with uint16. We can leverage ffmpeg to decode it. Here we give an example of decoding:

import ffmpeg
import numpy as np
out, _ = (
    ffmpeg
    .input(depth_path)
    .output('pipe:', format='rawvideo', pix_fmt='gray16le', loglevel="quiet")
    .run(capture_stdout=True)
)
video = np.frombuffer(out, np.uint16).reshape([-1, 1080, 1920]).copy()

3D Reconstructed Sequence: the 3D reconstructed sequence are also compressed as nut files. Each 3D sequence is compressed to three nut files. Our algorithm compress 3D mesh sequence with the same topology. Here we give an example of a compresses vertices to nut files:

Encode

from mesh_compression.encode import SequenceEncoder
encoder = SequenceEncoder('test_data/test', number_of_vertex)   # we have three video files, test_data/test_{0, 1, 2}.nut
for i in range(len(vertex_sequences)): # vertex_sequences has shape of frame_num * num_vertex * 3
    frame = vertex_sequences[i]
    encoder.write_frame(frame)

encoder.close()

Decode

from mesh_compression.decode import decode_nut_to_np
vertices = decode_nut_to_np('./test_data/test', number_of_vertex)

Example Data: We give example data in test_data folder, the reconstruct_filt_{0,1,2}.nut files are 3D reconstructed sequence files. The files with 000337 prefix are raw data (camera intrinsics, audio, landmarks, and Depth Sequence.)

Environments

  • For offline render, we need to install OSMesa, please follow the instructions of pyrender

  • For the differential render, we leverage nvdiffrast, please follow the instructions of nvdiffrast

  • Afterwards, run the other environments with pip install -r requirements.txt

  • Same as the instructions of Deep 3D Face Reconstruction.

    • Download the Basel Face Model. Due to the license agreement of Basel Face Model, you have to download the BFM09 model after submitting an application on its home page. After getting the access to BFM data, download "01_MorphableModel.mat" and put it into ./deep_3drecon/BFM subfolder.
    • Download Download the Expression Basis provided by Guo et al. You can find a link named "CoarseData" in the first row of Introduction part in their repository. Download and unzip the Coarse_Dataset.zip. Put "Exp_Pca.bin" into ./deep_3drecon/BFM subfolder. The expression basis are constructed using Facewarehouse data and transferred to BFM topology.
    • Leverage the transferBFM09 function of Deep3DFaceReconstruction repo to obtain "BFM/BFM_model_front.mat".

4D Reconstruction

The reconstruction code is implemented in the reconstruction folder. We respectively provide the code of reconstructing 3D faces from three RGBD cameras and one RGBD camera.

For three-camera reconstruction, run

python multicam_reconstruction.py --file_path ../test_data/000337 --save_path ../test_data/000337_save --faces_path ../test_data/faces.pkl

For one-camera reconstruction, run

python singlecam_reconstruction.py --file_path ../test_data/000337 --save_path ../test_data/000337_save --faces_path ../test_data/faces.pkl

After reconstruction, we leverage low pass filter to smooth the reconstructed results. The code is provided in smooth_4d.py. For example, run

python smooth_4d.py --file_path ../test_data/000337 --out_path ../test_data/000337_filt

Preprocess Azure Kinect RGBD files

With the recorded MKV files of azure kinect, we decode it to nut files, mp4 files, and wav files. The nut files records depth video, mp4 files record RGB video, wav files record audio. The preprocess code is provided in process_mkv.py. For people who wants to collect your own data, you can use this code for reference.

Visualize 3D Sequences

We visualize nut files in reconstruction/visualize_nut.py. Run python visualize_nut.py --file_path {} --out_path {} provides the visualization mp4 video. See reconstruction/visualize.sh for example.

License and Citation

@article{wu2023mmface4d,
  title={MMFace4D: A Large-Scale Multi-Modal 4D Face Dataset for Audio-Driven 3D Face Animation},
  author={Wu, Haozhe and Jia, Jia and Xing, Junliang and Xu, Hongwei and Wang, Xiangyuan and Wang, Jelo},
  journal={arXiv preprint arXiv:2303.09797},
  year={2023}
}