Papers › 3D Human Texture Estimation from a Single Image with Transformers

3D Human Texture Estimation from a Single Image with Transformers

6 Sep 2021ICCV 2021 10arXiv:2109.02563archive 2025-07-28

Xiangyu Xu, Chen Change Loy

We propose a Transformer-based framework for 3D human texture estimation from a single image. The proposed Transformer is able to effectively exploit the global information of the input image, overcoming the limitations of existing methods that are solely based on convolutional neural networks. In addition, we also propose a mask-fusion strategy to combine the advantages of the RGB-based and texture-flow-based models. We further introduce a part-style loss to help reconstruct high-fidelity colors without introducing unpleasant artifacts. Extensive experiments demonstrate the effectiveness of the proposed method against state-of-the-art 3D human texture estimation approaches both quantitatively and qualitatively.

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