GANtlitz: Ultra High Resolution Generative Model for Multi-Modal Face Textures
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
High-resolution texture maps are essential to render photoreal digital humans for visual effects or to generate data for machine learning. The acquisition of high resolution assets at scale is cumbersome, it involves enrolling a large number of human subjects, using expensive multi-view camera setups, and significant manual artistic effort to align the textures. To alleviate these problems, we introduce GANtlitz (A play on the german noun Antlitz, meaning face), a generative model that can synthesize multi-modal ultra-high-resolution face appearance maps for novel identities. Our method solves three distinct challenges: 1) unavailability of a very large data corpus generally required for training generative models, 2) memory and computational limitations of training a GAN at ultra-high resolutions, and 3) consistency of appearance features such as skin color, pores and wrinkles in high-resolution textures across different modalities. We introduce dual-style blocks, an extension to the style blocks of the StyleGAN2 architecture, which improve multi-modal synthesis. Our patch-based architecture is trained only on image patches obtained from a small set of face textures (<100) and yet allows us to generate seamless appearance maps of novel identities at 6k×4k resolution. Extensive qualitative and quantitative evaluations and baseline comparisons show the efficacy of our proposed system.
Description
CCS Concepts: Computing methodologies -> Machine learning; Texturing
@article{10.1111:cgf.15039,
journal = {Computer Graphics Forum},
title = {{GANtlitz: Ultra High Resolution Generative Model for Multi-Modal Face Textures}},
author = {Gruber, Aurel and Collins, Edo and Meka, Abhimitra and Mueller, Franziska and Sarkar, Kripasindhu and Orts-Escolano, Sergio and Prasso, Luca and Busch, Jay and Gross, Markus and Beeler, Thabo},
year = {2024},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.15039}
}