CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis
Abstract
In this work, we focus on a novel task of category-level functional hand-object manipulation synthesis covering both rigid and articulated object categories. Given an object geometry, an initial human hand pose as well as a sparse control sequence of object poses, our goal is to generate a physically reasonable hand-object manipulation sequence that performs like human beings. To address such a challenge, we first design CAnonicalized Manipulation Spaces (CAMS), a two-level space hierarchy that canonicalizes the hand poses in an object-centric and contact-centric view. Benefiting from the representation capability of CAMS, we then present a two-stage framework for synthesizing human-like manipulation animations. Our framework achieves state-of-the-art performance for both rigid and articulated categories with impressive visual effects. Codes and video results can be found at our project homepage: https://cams-hoi.github.io/.
Cite
Text
Zheng et al. "CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis." Conference on Computer Vision and Pattern Recognition, 2023. doi:10.1109/CVPR52729.2023.00064Markdown
[Zheng et al. "CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis." Conference on Computer Vision and Pattern Recognition, 2023.](https://mlanthology.org/cvpr/2023/zheng2023cvpr-cams/) doi:10.1109/CVPR52729.2023.00064BibTeX
@inproceedings{zheng2023cvpr-cams,
title = {{CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis}},
author = {Zheng, Juntian and Zheng, Qingyuan and Fang, Lixing and Liu, Yun and Yi, Li},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2023},
pages = {585-594},
doi = {10.1109/CVPR52729.2023.00064},
url = {https://mlanthology.org/cvpr/2023/zheng2023cvpr-cams/}
}