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aaronjackson/vrn

★ 4,521MATLABAudience · researcherComplexity · 4/5LicenseSetup · hard

TLDR

A 2017 research project that reconstructs a 3D face model from a single photograph using a convolutional neural network, with MATLAB and Python workflows and a Docker CPU option.

Mindmap

mindmap
  root((VRN Face Recon))
    What It Does
      Single photo input
      3D volume output
      Mesh extraction
    Requirements
      NVIDIA GPU
      CUDA 7.5 or 8.0
      CuDNN 5.1
    Running It
      MATLAB workflow
      Python shell script
      Docker CPU version
    Research Context
      ICCV 2017 paper
      Volumetric method
      Example images included
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Things people build with this

USE CASE 1

Reconstruct a 3D face mesh from a single portrait photo for research or animation purposes

USE CASE 2

Benchmark 3D face reconstruction accuracy on standard datasets using the provided MATLAB scripts

USE CASE 3

Try face reconstruction via the Docker CPU image without setting up GPU dependencies

Tech stack

MATLABPythonTorch7CUDACuDNNDocker

Getting it running

Difficulty · hard Time to first run · 1h+

Requires CUDA GPU with specific CUDA 7.5/8.0 and CuDNN 5.1 versions, Docker image available for CPU-only testing.

Free to use and modify for any purpose, including commercial use, as long as you keep the copyright notice (MIT License).

In plain English

VRN is a research project that takes a regular photograph of a face and produces a three-dimensional model of it. The full name is Volumetric Regression Network, and the core technique treats face reconstruction as a segmentation problem rather than a geometric estimation problem. It produces a 3D volume spatially aligned with the input image, and you can extract a surface mesh from that volume. The code was developed for a paper published at an international computer vision conference in 2017. The authors released both MATLAB and Python workflows. MATLAB produces higher-quality results and was used for the paper's measurements, so the README recommends it for any work where accuracy matters. A Python and shell script alternative exists for users who do not have a MATLAB license. Getting the code running requires a GPU. The software is built on Torch7, a machine learning framework, and needs CUDA (NVIDIA's GPU computing platform) at version 7.5 or 8.0 along with CuDNN version 5.1. The README provides a detailed installation walkthrough tested on Fedora and CentOS Linux and notes that Windows is unlikely to work. A Docker image for CPU-only use is available on Docker Hub for anyone who wants to try the reconstruction without going through GPU setup. Several example face images from a standard academic dataset are included in the repository. The neural network model weights must be downloaded separately using an included download script. An online demo on the project's website lets anyone try the reconstruction in a browser without installing anything locally. The code is released under the MIT License, which allows free use and modification for any purpose as long as the copyright notice is kept.

Copy-paste prompts

Prompt 1
I have portrait photos and want to generate 3D face meshes from them using VRN. Walk me through setting up the Docker CPU version and running it on a folder of images.
Prompt 2
Show me how to install Torch7 with CUDA on Ubuntu and run the VRN face reconstruction on the included example images using MATLAB.
Prompt 3
I want to use the 3D mesh output from VRN in Blender. How do I convert the volumetric output to an OBJ or PLY format that Blender can import?
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