Refining 3D Human Texture Estimation from a Single Image

• URL: http://arxiv.org/abs/2303.03471v1
• Date: Mon, 6 Mar 2023 19:53:50 GMT
• Title: Refining 3D Human Texture Estimation from a Single Image
• Authors: Said Fahri Altindis, Adil Meric, Yusuf Dalva, Ugur Gudukbay, Aysegul Dundar
• Abstract summary: Estimating 3D human texture from a single image is essential in graphics and vision. We propose a framework that adaptively samples the input by a deformable convolution where offsets are learned via a deep neural network.
• Score: 3.8761064607384195
• License: http://creativecommons.org/licenses/by/4.0/
• 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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