This repository features code for 3D face reconstruction accuracy evaluation. We hope to provide an automated benchmark for researchers in the area of 3D face reconstruction.
Method and formulas for evaluation are detailed in paper 'MobileFace: 3D Face Reconstruction with Efficient CNN Regression' presented at PeopleCap 2018, an ECCV workshop on capturing and modeling human bodies, faces and hands.
@InProceedings{Chinaev2018MobileFace,
author = {Chinaev, Nikolai and Chigorin, Alexander and Laptev, Ivan},
title = {MobileFace: 3D Face Reconstruction with Efficient CNN Regression},
booktitle = {European Conference on Computer Vision (ECCV) Workshops},
month = {Sep},
year = {2018}
}
The evaluation is performed on a subset of BU4DFE. The subset is divided into two splits: validation and test, 2989 scans in total.
The two splits are defined as lists of images stored in data/val_list.txt and data/test_list.txt. Filenames in these two files reflect paths to the images in BU4DFE dataset. For example, F002_Angry_000_3d.jpg -> F002/Angry/000.jpg
The code allows to test the accuracy of 3D models reconstructed for these images.
Access to BU4DFE dataset is needed in order to run this code.
- In folder data create subfolders
scans,indices,csv,scans_cropped. - Unpack files listed in data/wrl_files_list.txt into data/scans, rename them as in F001/Angry/036.wrl -> F001_Angry_036_3d.wrl
- Unpack indices into data/indices
- Unpack csv files into data/csv
- run
transformWrl.py - run
cropScans.m
Run compareBu4dfeMeshes.m, follow instructions from this file.