  We propose SMPLitex, a method for estimating and manipulating the complete 3D
appearance of humans captured from a single image. SMPLitex builds upon the
recently proposed generative models for 2D images, and extends their use to the
3D domain through pixel-to-surface correspondences computed on the input image.
To this end, we first train a generative model for complete 3D human
appearance, and then fit it into the input image by conditioning the generative
model to the visible parts of the subject. Furthermore, we propose a new
dataset of high-quality human textures built by sampling SMPLitex conditioned
on subject descriptions and images. We quantitatively and qualitatively
evaluate our method in 3 publicly available datasets, demonstrating that
SMPLitex significantly outperforms existing methods for human texture
estimation while allowing for a wider variety of tasks such as editing,
synthesis, and manipulation
