July. 8th, 2024: 🌟We released our source code!
Reconstructing high-fidelity hand models with intricate tex- tures plays a crucial role in enhancing human-object interac- tion and advancing real-world applications. Despite the state- of-the-art methods excelling in texture generation and image rendering, they often face challenges in accurately capturing geometric details. Learning-based approaches usually offer better robustness and faster inference, which tend to produce smoother results and require substantial amounts of training data. To address these issues, we present a novel fine-grained multi-view hand mesh reconstruction method that leverages inverse rendering to restore hand poses and intricate details. Firstly, our approach predicts a parametric hand mesh model through Graph Convolutional Networks (GCN) based method from multi-view images. We further introduce a novel Hand Albedo and Mesh (HAM) optimization module to refine both the hand mesh and textures, which is capable of preserv- ing the mesh topology. In addition, we suggest an effec- tive mesh-based neural rendering scheme to simultaneously generate photo-realistic image and optimize mesh geometry by fusing the pre-trained rendering network with vertex fea- tures. We conduct the comprehensive experiments on Inter- Hand2.6M, DeepHandMesh and dataset collected by ourself, whose promising results show that our proposed approach outperforms the state-of-the-art methods on both reconstruc- tion accuracy and rendering quality.
Overview of our coarse-to-fine framework. Given a set of calibrated images, we initialize MANO parameters and refine the mesh using our proposed HAM module and inverse rendering to achieve geometric details. By jointly optimizing the mesh using a model-based neural rendering, a fine-grained mesh can be obtained along with its hyper-realistic rendered images.
Notes:
- All the experiments are performed on 1 NVIDIA GeForce RTX 3090Ti GPU.
a. Create a conda virtual environment and install required packages.
git clone [email protected]:agnJason/FMHR.git
conda create -n FMHR python=3.10 -y
conda activate FMHR
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirement.txtb. Prepare MANO models.
Besides, you also need to download the MANO model. Please visit the MANO website and register to get access to the downloads section. You need to put MANO_RIGHT.pkl and MANO_LEFT.pkl under the ./mano folder.
Edit your Interhand2.6M PATH in conf/ih_sfs.conf->data_path, which should contain ./images and ./annotations.
# Mesh optim with MANO annotations, change capture/name in conf/ih_sfs.conf
python mesh_sfs_optim.py --conf conf/ih_sfs.conf --scan_id 0
# Train Neural renderer
python neural_render.py --conf conf/ih_sfs.conf --scan_id 0 --net_type mlpThe output should be in ./interhand_out.
Prepare MANO paramters.
# optim 3d pose
python pose_optim.py --data_path ./demo_data --scan_id 1 --out_path ./demo_out
# optim mano para
python mano_optim.py --data_path ./demo_data --scan_id 1 --out_path ./demo_outMesh optim.
# Mesh optim
python mesh_sfs_optim.py --conf ./conf/demo_sfs.conf --data_path ./demo_data --scan_id 1
The output will be in ./demo_out.
(Optional) MANO parameters can also be predicted by GCN-base network.
python multihands_mano.py --conf ./conf/demo_sfs.conf --data_path ./demo_data --scan_id 1If you find our project is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@inproceedings{gan2024fine,
title={Fine-Grained Multi-View Hand Reconstruction Using Inverse Rendering},
author={Gan, Qijun and Li, Wentong and Ren, Jinwei and Zhu, Jianke},
booktitle={AAAI},
pages={1779--1787},
year={2024}
}