Uncertainty-Aware Adjustment via Learnable Coefficients for Detailed 3D Reconstruction of Clothed Humans from Single Images

Simple item page
dc.contributor.authorYang, Yadanen_US
dc.contributor.authorLi, Yunzeen_US
dc.contributor.authorYing, Fanglien_US
dc.contributor.authorPhaphuangwittayakul, Aniwaten_US
dc.contributor.authorDhuny, Riyaden_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.date.accessioned2025-10-07T05:01:56Z
dc.date.available2025-10-07T05:01:56Z
dc.date.issued2025
dc.description.abstractAlthough single-image 3D human reconstruction has made significant progress in recent years, few of the current state-of-theart methods can accurately restore the appearance and geometric details of loose clothing. To achieve high-quality reconstruction of a human body wearing loose clothing, we propose a learnable dynamic adjustment framework that integrates side-view features and the uncertainty of the parametric human body model to adaptively regulate its reliability based on the clothing type. Specifically, we first adopt the Vision Transformer model as an encoder to capture the image features of the input image, and then employ SMPL-X to decouple the side-view body features. Secondly, to reduce the limitations imposed by the regularization of the parametric model, particularly for loose garments, we introduce a learnable coefficient to reduce the reliance on SMPLX. This strategy effectively accommodates the large deformations caused by loose clothing, thereby accurately expressing the posture and clothing in the image. To evaluate the effectiveness, we validate our method on the public CLOTH4D and Cape datasets, and the experimental results demonstrate better performance compared to existing approaches. The code is available at https://github.com/yyd0613/CoRe-Human.en_US
dc.description.number7
dc.description.sectionheadersDigital Human
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70239
dc.identifier.issn1467-8659
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70239
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70239
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → Mesh models
dc.subjectComputing methodologies → Mesh models
dc.titleUncertainty-Aware Adjustment via Learnable Coefficients for Detailed 3D Reconstruction of Clothed Humans from Single Imagesen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
cgf70239.pdf
Size:
10.62 MB
Format:
Adobe Portable Document Format
Download
Collections
44-Issue 7