Refining 3D Human Texture Estimation From a Single Image

Estimating 3D human texture from a single image is essential in graphics and vision. It requires learning a mapping function from input images of humans with diverse poses into the parametric ( uv ) space and reasonably hallucinating invisible parts. To achieve a high-quality 3D human texture estima...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence 2024-12, Vol.46 (12), p.11464-11475
Hauptverfasser: Altindis, Said Fahri, Meric, Adil, Dalva, Yusuf, Gudukbay, Ugur, Dundar, Aysegul
Format: Artikel
Sprache:eng
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Zusammenfassung:Estimating 3D human texture from a single image is essential in graphics and vision. It requires learning a mapping function from input images of humans with diverse poses into the parametric ( uv ) space and reasonably hallucinating invisible parts. To achieve a high-quality 3D human texture estimation, we propose a framework that adaptively samples the input by a deformable convolution where offsets are learned via a deep neural network. Additionally, we describe a novel cycle consistency loss that improves view generalization. We further propose to train our framework with an uncertainty-based pixel-level image reconstruction loss, which enhances color fidelity. We compare our method against the state-of-the-art approaches and show significant qualitative and quantitative improvements.
ISSN:0162-8828
1939-3539
1939-3539
2160-9292
DOI:10.1109/TPAMI.2024.3456817