mlsanityData to 14 Oct 2026

Refining 3D Human Texture Estimation from a Single Image

arXiv 2303.03471 ↗First posted 6 Mar 2023cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 56 of its 72 references that are in the map. First posted on arXiv 6 Mar 2023.

Builds on

56 of 72 references.

References · 56

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38 more
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2025MD-ProjTex: Texturing 3D Shapes with Multi-Diffusion Projectioncited by 2
  2. 2023Diverse Semantic Image Editing with Style Codescited by 1