ContactGen: Generative Contact Modeling for Grasp Generation

Shaowei Liu, Yang Zhou, Jimei Yang, Saurabh Gupta, Shenlong Wang

ICCV 2023 pp. 20609-20620

doi:10.1109/ICCV51070.2023.01884 /iccv/2023/liu2023iccv-contactgen/

Abstract

This paper presents a novel object-centric contact representation ContactGen for hand-object interaction. The ContactGen comprises 3 components: a contact map indicates the contact location, a part map represents the contact hand part, and a direction map tells the contact direction within each part. Given an input object, we propose a conditional generative model to predict ContactGen and adopt model-based optimization to predict diverse and geometrically feasible grasps. Experimental results demonstrate our method can generate high-fidelity and diverse human grasps for various objects.

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Cite

Text

Liu et al. "ContactGen: Generative Contact Modeling for Grasp Generation." International Conference on Computer Vision, 2023. doi:10.1109/ICCV51070.2023.01884

Markdown

[Liu et al. "ContactGen: Generative Contact Modeling for Grasp Generation." International Conference on Computer Vision, 2023.](https://mlanthology.org/iccv/2023/liu2023iccv-contactgen/) doi:10.1109/ICCV51070.2023.01884

BibTeX

@inproceedings{liu2023iccv-contactgen,
  title     = {{ContactGen: Generative Contact Modeling for Grasp Generation}},
  author    = {Liu, Shaowei and Zhou, Yang and Yang, Jimei and Gupta, Saurabh and Wang, Shenlong},
  booktitle = {International Conference on Computer Vision},
  year      = {2023},
  pages     = {20609-20620},
  doi       = {10.1109/ICCV51070.2023.01884},
  url       = {https://mlanthology.org/iccv/2023/liu2023iccv-contactgen/}
}