This is the official repository for evaluation on the NoW Benchmark. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.
Given a single monocular image, the challenge consists of reconstructing a 3D face. Since the predicted meshes occur in different local coordinate systems, the reconstructed 3D mesh is rigidly aligned (rotation, translation, and scaling) to the scan using a set of corresponding landmarks between the prediction and the scan. We further perform a rigid alignment based on the scan-to-mesh distance (which is the absolute distance between each scan vertex and the closest point in the mesh surface) between the ground truth scan, and the reconstructed mesh using the landmarks alignment as initialization. For more details, see the NoW Website or the RingNet paper.
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
Soubhik Sanyal, Timo Bolkart, Haiwen Feng, Michael J. Black
Computer Vision and Pattern Recognition (CVPR) 2019
git clone https://github.com/soubhiksanyal/now_evaluation.git
Please install the virtual environment
mkdir <your_home_dir>/.virtualenvs
python3 -m venv <your_home_dir>/.virtualenvs/now_evaluation
source <your_home_dir>/.virtualenvs/now_evaluation/bin/activate
Make sure your pip version is up-to-date:
pip install -U pip
Install the requirements by using:
pip install -r requirements.txt
Install mesh processing libraries from MPI-IS/mesh within the virtual environment.
Clone the flame-fitting repository and copy the required folders by the following comments
git clone https://github.com/Rubikplayer/flame-fitting.git
cp -r flame-fitting/smpl_webuser now_evaluation/smpl_webuser
cp -r flame-fitting/sbody now_evaluation/sbody
Clone Eigen, checkout v3.4.0, and copy the it to the following folder
git clone https://gitlab.com/libeigen/eigen.git
cd eigen
git checkout 3.4.0
cd ..
cp -r eigen now_evaluation/sbody/alignment/mesh_distance/eigen
Edit the file 'now_evaluation/sbody/alignment/mesh_distance/setup.py' to set EIGEN_DIR to the location of Eigen. Then compile the code by following command
cd now_evaluation/sbody/alignment/mesh_distance
make
The installation of Scan2Mesh is followed by the codebase provided by flame-fitting. Please check that repository for more detailed instructions on Scan2Mesh installation.
Download the NoW Dataset and the validation set scans from the NoW website, and predict 3D faces for all validation images.
Before running the now evaluation,
1) check that the predicted meshes can be successfuly loaded by the used mesh loader by running
python check_predictions.py <predicted_mesh_path>
Running this loads the <predicted_mesh_path> mesh and exports it to ./predicted_mesh_export.obj. Please check if this file can be loaded by e.g. MeshLab or any other mesh loader, and that the resulting mesh looks like the input mesh.
2) check that the landmarks for the predicted meshes are correct by running
python check_predictions.py <predicted_mesh_path> <predicted_mesh_landmark_path> <gt_scan_path> <gt_lmk_path>
Running this loads the <predicted_mesh_path> mesh, rigidly aligns it with the the scan <gt_scan_path>, and outputs the aligned mesh to ./predicted_mesh_aligned.obj, and the cropped scan to ./cropped_scan.obj. Please check if the output mesh and scan are rigidly aligned by jointly opening them in e.g. MeshLab.
To run the now evaluation on the validation set, run
python compute_error.py
The function in metric_computation() in compute_error.py is used to compute the error metric. In the most common setting you can run python compute_error.py --predicted_mesh_folder <predicted_mesh_folder> --dataset_folder <dataset_folder>. For more options please see compute_error.py
The predicted_mesh_folder should in a similar structure as mentioned in the RingNet website.
Prior to computing the point-to-surface distance, a rigid alignment between each predicted mesh and the scan is computed. The rigid alignment computation requires for each predicted mesh a file with following 7 landmarks:
Visualization of the reconstruction error is best done with a cumulative error curve. To generate a cumulative error plot, call generating_cumulative_error_plots() in the cumulative_errors.py with the list of output files and the corresponding list method names.
Note that ground truth scans are only provided for the validation set. In order to participate in the NoW challenge, please submit the test set predictions to [email protected] as described here.
The used mesh loader is unable to load OBJ files with vertex colors appended to the vertices. I.e. if the OBJ contains lines of the following format v vx vy vz cr cg cb\n, export the meshes without vertex colors.
By using the NoW dataset or code, you acknowledge that you have read the license terms, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not use the code.
This codebase was developed for evaluation of the RingNet project. When using the code or NoW evaluation results in a scientific publication, please cite
@inproceedings{RingNet:CVPR:2019,
title = {Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision},
author = {Sanyal, Soubhik and Bolkart, Timo and Feng, Haiwen and Black, Michael},
booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
month = jun,
year = {2019},
month_numeric = {6}
}