AD-NeRF

PyTorch implementation for the paper "AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis"

Prerequisites

  • You can create an anaconda environment called adnerf with:

    conda env create -f environment.yml
conda activate adnerf

  • PyTorch3D

    Recommend install from a local clone

    git clone https://github.com/facebookresearch/pytorch3d.git
cd pytorch3d && pip install -e .

  • Basel Face Model 2009

    Put "01_MorphableModel.mat" to data_util/face_tracking/3DMM/; cd data_util/face_tracking; run

    python convert_BFM.py

Train AD-NeRF

  • Data Preprocess ($id Obama for example)

    bash process_data.sh Obama
    • Input: A portrait video at 25fps containing voice audio. (dataset/vids/$id.mp4)
    • Output: folder dataset/$id that contains all files for training

  • Train Two NeRFs (Head-NeRF and Torso-NeRF)

    • Train Head-NeRF with command

      python NeRFs/HeadNeRF/run_nerf.py --config dataset/$id/HeadNeRF_config.txt

    • Copy latest trainied model from dataset/$id/logs/$id_head to dataset/$id/logs/$id_com

    • Train Torso-NeRF with command

      python NeRFs/TorsoNeRF/run_nerf.py --config dataset/$id/TorsoNeRF_config.txt

Run AD-NeRF for rendering

  • Reconstruct original video with audio input

    python NeRFs/TorsoNeRF/run_nerf.py --config dataset/$id/TorsoNeRFTest_config.txt --aud_file=dataset/$id/aud.npy --test_size=300

  • Drive the target person with another audio input

    python NeRFs/TorsoNeRF/run_nerf.py --config dataset/$id/TorsoNeRFTest_config.txt --aud_file=${deepspeechfile.npy} --test_size=-1

Acknowledgments

We use face-parsing.PyTorch for parsing head and torso maps, and DeepSpeech for audio feature extraction. The NeRF model is implemented based on NeRF-pytorch.

GitHub

https://github.com/YudongGuo/AD-NeRF