Awesome

hand-reconstruction

The official project repository for the BMVC 2019 paper
Single Image 3D Hand Reconstruction with Mesh Convolutions
by Dominik Kulon, Haoyang Wang, Riza Alp Güler, Michael Bronstein, Stefanos Zafeiriou

Introduction

Monocular 3D reconstruction of deformable objects, such as human body parts, has been typically approached by predicting parameters of heavyweight linear models. In this paper, we demonstrate an alternative solution that is based on the idea of encoding images into a latent non-linear representation of meshes. The prior on 3D hand shapes is learned by training an autoencoder with intrinsic graph convolutions performed in the spectral domain. The pre-trained decoder acts as a non-linear statistical deformable model. The latent parameters that reconstruct the shape and articulated pose of hands in the image are predicted using an image encoder.

Data

The data can be downloaded from the link below:
Hand Reconstruction Data

To access the data, please, contact me using the email address provided in the paper.

The data directory should be placed in the root of the project structure.

• ./data/datasets/hand-panoptic - Training data for the image-to-mesh system.

• ./data/datasets/mesh-samples - Data for pretraining the mesh autoencoder.

• ./data/models/i2m - The image-to-mesh network.

• ./data/models/spectral-ae - Spectral autoencoder.

• ./data/template - Hand template and mesh convolutions operators.

Requirements

Python 3.6.7
Tensorflow 1.14.0
NumPy
SciPy

Visualization:
Matplotlib
OpenCV

Optional:
For generating convolution and sampling operators: COMA
For visualizing the latent space as shown in the screenshot below: traits, mayavi

Running the code

Testing:

• ./evaluation/spectral_ae.ipynb - Example of running the trained spectral autoencoder and visualizing the output.
• ./evaluation/i2m.ipynb - Example of running the trained image-to-mesh system and visualizing the output.

Training:

• ./training/spectral/method/train_spectral_ae.py - Code for training the spectral autoencoder.
• ./training/i2m/method/train_i2m_spectral_densenet.py - Code for training the image to mesh system.

License

If you use the code, please, make sure you are familiar with the LICENSE and cite our paper.

Single Image 3D Hand Reconstruction with Mesh Convolutions

@inproceedings{dkulon2019rec,
author = {Dominik Kulon and
Haoyang Wang and
Riza Alp G{\"{u}}ler and
Michael M. Bronstein and
Stefanos Zafeiriou},
title = {Single Image 3D Hand Reconstruction with Mesh Convolutions},
booktitle = {Proceedings of the British Machine Vision Conference ({BMVC})},
year = {2019}
}

The training images come from the CMU Panoptic Dataset, training meshes are derivatives of MANO, and the mesh autoencoder architecture is based on COMA.