Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric Regression

Aaron S. Jackson, Adrian Bulat, Vasileios Argyriou and Georgios Tzimiropoulos

Try out the code without running it! Check out our online demo here.

[[http://aaronsplace.co.uk/papers/jackson2017recon/preview.png]]

Please visit our project webpage for a link to the paper and an example video run on 300VW. This code is licenses under the MIT License, as described in the LICENSE file.

This is an unguided version of the Volumetric Regression Network (VRN) for 3D face reconstruction from a single image. This method approaches the problem of reconstruction as a segmentation problem, producing a 3D volume, spatially aligned with the input image. A mesh can then be obtained by taking the isosurface of this volume.

Several example images are included in the examples folder. Most of these are AFLW images taken from 3DDFA.

If you are running the code to calculate error for a potential publication, please use the MATLAB version, as this is what was used to compute the error for the paper.

Software Requirements

• pytorch (>=0.2 recommended)
• Python 3.5+ or Python 2.7 (it may work with other versions too)
• Linux or macOS (windows may work once pytorch gets supported)

Getting Started

Download model

URL[链接]: [https://pan.baidu.com/s/1bqQSxQZ](https://pan.baidu.com/s/1bqQSxQZ) PWD[密码]: mqs9

Running with Python

python demo.py

Acknowledgements

• 2D and 3D Face alignment library build using pytorch
• Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric Regression