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arXiv · 2303.03471

Refining 3D Human Texture Estimation from a Single Image

Said Fahri Altindis·Adil Meric·Yusuf Dalva·Ugur Gudukbay·Aysegul Dundar
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Abstract

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.

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Said Fahri Altindis, Adil Meric, Yusuf Dalva, Ugur Gudukbay, Aysegul Dundar. 2023-03-06. Refining 3D Human Texture Estimation from a Single Image. https://arxiv.org/abs/2303.03471

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