jackweiwang / SynergyNet

3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

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SynergyNet

3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

Cho-Ying Wu, Qiangeng Xu, Ulrich Neumann, CGIT Lab at University of Souther California

[paper] [project page]

This paper supersedes the previous version of M3-LRN.

Advantages:

SOTA on all 3D facial alignment, face orientation estimation, and 3D face modeling.

Fast inference with 3000fps on a laptop RTX 2080 Ti.

Simple implementation with only widely used operations.

Evaluation (This project is built/tested on Python 3.8 and PyTorch 1.9)

  1. Clone

    git clone https://github.com/choyingw/SynergyNet

    cd SynergyNet

  2. Use conda

    conda create --name SynergyNet

    conda activate SynergyNet

  3. Install pre-requisite common packages

    PyTorch 1.9 (should also be compatiable with 1.0+ versions), Opencv, Scipy, Matplotlib

  4. Prepare data

Download data [here] and [here]. Extract these data under the repo root.

These data are processed from [3DDFA] and [FSA-Net].

Download pretrained weights [here]. Put the model under 'models/'

  1. Benchmarking

    python benchmark.py -w pretrained/best.pth.tar

Print-out results and visualization under 'results/' (see 'demo/' for some sample references) are shown.

TODO

  • Single-Image inference
  • Add a renderer and 3D face output
  • Training script
  • Texture synthesis in the supplementary

More Results

Facial alignemnt on AFLW2000-3D (NME of facial landmarks):

Face orientation estimation on AFLW2000-3D (MAE of Euler angles):

Results on artistic faces:

Related Project

[Voice2Mesh] (analysis on relation for voice and 3D face)

Acknowledgement

The project is developed on [3DDFA] and [FSA-Net]. Thank them for their wonderful work.

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3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

License:MIT License


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