Canonical pose reconstruction from single depth image for 3D non-rigid pose recovery on limited datasets

Alhamazani, Fahd, Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 and Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 2025. Canonical pose reconstruction from single depth image for 3D non-rigid pose recovery on limited datasets. Computers & Graphics 132 , 104370. 10.1016/j.cag.2025.104370 Item availability restricted.

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Abstract

3D reconstruction from 2D inputs, especially for non-rigid objects like humans, presents unique challenges due to the significant range of possible deformations. Traditional methods often struggle with non-rigid shapes, which require extensive training data to cover the entire deformation space. This study addresses these limitations by proposing a canonical pose reconstruction model that transforms single-view depth images of deformable shapes into a canonical form. This alignment facilitates shape reconstruction by enabling the application of rigid object reconstruction techniques, and supports recovering the input pose in voxel representation as part of the reconstruction task, utilising both the original and deformed depth images. Notably, our model achieves effective results with using a small dataset with 300 samples in total, containing variations in shape (obese, slim and fit bodies) and gender (female and male) and size (child and adult). Experimental results on animal and human datasets demonstrate that our model outperforms other state-of-the-art methods.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0097-8493
Funders:	The Royal Society
Date of First Compliant Deposit:	27 September 2025
Date of Acceptance:	4 August 2025
Last Modified:	29 Sep 2025 11:30
URI:	https://orca.cardiff.ac.uk/id/eprint/181363

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