Awesome
High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition
Introduction
This repo is official implementation of High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition(CVPR 2023).
Install environment
conda env create -f environment.yml
Dataset
We use subdivided MANO to register 3D hand Mesh from "DeepHandMesh". Download joint annotation and our processed dataset from here.
After downloading, put the contents in $root/data
directory.
Download images for DeephandMesh and following the unzip instructions. Then, put the images
folder under $root/data/DeepHandMesh
directory
Assets
Download subdivided MANO from here and put the contents under $root/assets/
.
Download MANO from here. Put models
folder under $root/assets/mano
.
Pretrained Model
Download pretrained model from here and put the contents under $root/model
.
Testing
python main.py --test --nmp --nrd
Training
python main.py --nmp --nrd
Reference
@InProceedings{Luan_2023_CVPR,
author = {Luan, Tianyu and Zhai, Yuanhao and Meng, Jingjing and Li, Zhong and Chen, Zhang and Xu, Yi and Yuan, Junsong},
title = {High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {16795-16804}
}
Acknowledgements
This repo inherited code from DeepHandMesh and S2HAND.