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<br /> <p align="center"> <h1 align="center">ContactGen: Generative Contact Modeling <br>for Grasp Generation</h1> <p align="center"> <a href="https://stevenlsw.github.io"><strong>Shaowei Liu</strong></a> · <a href="https://yangzhou95.github.io/"><strong>Yang Zhou</strong></a> · <a href="https://jimeiyang.github.io/"><strong>Jimei Yang</strong></a> · <a href="https://saurabhg.web.illinois.edu/"><strong>Saurabh Gupta*</strong></a> · <a href="https://shenlong.web.illinois.edu/"><strong>Shenlong Wang*</strong></a> · </p> <p align="center"> <img src="assets/teaser.png" alt="Logo" width="80%"> </p> <p align="center"> <a href='https://arxiv.org/abs/2310.03740'> <img src='https://img.shields.io/badge/Paper-PDF-green?style=flat&logo=arXiv&logoColor=green' alt='Paper PDF'> </a> <a href='https://stevenlsw.github.io/contactgen/' style='padding-left: 0.5rem;'> <img src='https://img.shields.io/badge/Project-Page-blue?style=flat&logo=Google%20chrome&logoColor=blue' alt='Project Page'> <a href='https://youtu.be/pBgaQdMdB3Q' style='padding-left: 0.5rem;'> <img src='https://img.shields.io/badge/Youtube-Video-red?style=flat&logo=youtube&logoColor=red' alt='Youtube Video'> </a> </p> </p> <br />This repository contains the pytorch implementation for the paper ContactGen: Generative Contact Modeling for Grasp Generation, ICCV 2023. In this paper, we present a novel object-centric contact representation for high-fidelity and diverse human grasp synthesis of 3D objects.<br><br>
Installation
- Clone this repository:
git clone https://github.com/stevenlsw/contactgen.git cd contactgen - Install requirements by the following commands:
conda create -n contactgen python=3.9 conda activate contactgen pip3 install torch # install compatible version pip install "git+https://github.com/facebookresearch/pytorch3d.git" pip install -r requirements.txt cd pointnet_lib && python setup.py install
Demo
-
Generate grasp for toothpaste from sampled ContactGen. results are stored in
save_root.python demo.py --obj_path assets/toothpaste.ply --n_samples=10 --save_root exp/demo_results -
Below shows some generated samples for toothpaste:
1 2 3 4 
-
Visualize the generated grasps in
meshlabor by the following command usingopen3d.python vis_grasp.py --hand_path exp/demo_results/grasp_0.obj --obj_path assets/toothpaste.ply
Training & Inference
-
Download the processed GRAB dataset from here and unzip to current directory.
-
Train the model by the following command, experiment logs are stored in
work_dir.python train.py --work_dir exp -
Inference using the following command, generated samples are stored in
save_root.python eval.py --save_root exp/results --checkpoint exp/checkpoint.pt -
Pretrained models can be found at
checkpoint/checkpoint.pt
Citation
If you find our work useful in your research, please cite:
@inproceedings{liu2023contactgen,
title={ContactGen: Generative Contact Modeling for Grasp Generation},
author={Liu, Shaowei and Zhou, Yang and Yang, Jimei and Gupta, Saurabh and Wang, Shenlong},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year={2023}
}
Acknowledges
We thank:
- Manopth for ManoLayer implementation
- GrabNet for training and testing on GRAB dataset
- ContactOpt for contact map computation
- HALO for grasp evaluation setup
- LatentHuman for SDF model implementation