andersonyangoh/audio-driven-talkingface-headpose

Code for "Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose"

Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose

We provide PyTorch implementations for our arxiv paper "Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose"(http://arxiv.org/abs/2002.10137).

Note that this code is protected under patent. It is for research purposes only at your university (research institution) only. If you are interested in business purposes/for-profit use, please contact Prof.Liu (the corresponding author, email: [email protected]).

We provide a demo video here (please search for "Talking Face" in this page and click the "demo video" button).

Colab

Our Proposed Framework

Prerequisites

• Linux or macOS
• NVIDIA GPU
• Python 3
• MATLAB

Getting Started

Installation

• You can create a virtual env, and install all the dependencies by

pip install -r requirements.txt

Download pre-trained models

• Including pre-trained general models and models needed for face reconstruction, identity feature extraction etc
• Download from BaiduYun(extract code:usdm) or GoogleDrive and copy to corresponding subfolders (Audio, Deep3DFaceReconstruction, render-to-video).

Download face model for 3d face reconstruction

• Download Basel Face Model from https://faces.dmi.unibas.ch/bfm/main.php?nav=1-0&id=basel_face_model, and copy 01_MorphableModel.mat to Deep3DFaceReconstruction/BFM folder
• Download Expression Basis from CoarseData of Guo et al., and copy Exp_Pca.bin to Deep3DFaceReconstruction/BFM folder

Fine-tune on a target peron's short video

• 1. Prepare a talking face video that satisfies: 1) contains a single person, 2) 25 fps, 3) longer than 12 seconds, 4) without large body translation (e.g. move from the left to the right of the screen). An example is here. Rename the video to [person_id].mp4 (e.g. 1.mp4) and copy to Data subfolder.

Note: You can make a video to 25 fps by

ffmpeg -i xxx.mp4 -r 25 xxx1.mp4

• 2. Extract frames and lanmarks by

cd Data/
python extract_frame1.py [person_id].mp4

• 3. Conduct 3D face reconstruction. First should compile code in Deep3DFaceReconstruction/tf_mesh_renderer/mesh_renderer/kernels to .so, following its readme, and modify line 28 in rasterize_triangles.py to your directory. Then run

cd Deep3DFaceReconstruction/
CUDA_VISIBLE_DEVICES=0 python demo_19news.py ../Data/[person_id]

This process takes about 2 minutes on a Titan Xp.

• 4. Fine-tune the audio network. First modify line 28 in rasterize_triangles.py to your directory. Then run

cd Audio/code/
python train_19news_1.py [person_id] [gpu_id]

The saved models are in Audio/model/atcnet_pose0_con3/[person_id]. This process takes about 5 minutes on a Titan Xp.

• 5. Fine-tune the gan network. Run

cd render-to-video/
python train_19news_1.py [person_id] [gpu_id]

The saved models are in render-to-video/checkpoints/memory_seq_p2p/[person_id]. This process takes about 40 minutes on a Titan Xp.

Test on a target peron

Place the audio file (.wav or .mp3) for test under Audio/audio/. Run [with generated poses]

cd Audio/code/
python test_personalized.py [audio] [person_id] [gpu_id]

or [with poses from short video]

cd Audio/code/
python test_personalized2.py [audio] [person_id] [gpu_id]

This program will print 'saved to xxx.mov' if the videos are successfully generated. It will output 2 movs, one is a video with face only (_full9.mov), the other is a video with background (_transbigbg.mov).

Colab

A colab demo is here.

Acknowledgments

The face reconstruction code is from Deep3DFaceReconstruction, the arcface code is from insightface, the gan code is developed based on pytorch-CycleGAN-and-pix2pix.