  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.
