SMPLitex: A Generative Model and Dataset for 3D Human Texture Estimation from Single Image
Dan Casas (Universidad Rey Juan Carlos),* Marc Comino-Trinidad (Universidad Rey Juan Carlos)
The 34th British Machine Vision Conference
Abstract
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 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.Video
Citation
@inproceedings{Casas_2023_BMVC,
author = {Dan Casas and Marc Comino-Trinidad},
title = {SMPLitex: A Generative Model and Dataset for 3D Human Texture Estimation from Single Image},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0272.pdf}
}
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