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Authors: Milena Bagdasarian 1 ; Peter Eisert 1 ; 2 and Anna Hilsmann 1

Affiliations: 1 Fraunhofer Heinrich-Hertz-Institute, Berlin, Germany ; 2 Humboldt University of Berlin, Germany

Keyword(s): Texture Super-Resolution, Differentiable Rendering, GAN.

Abstract: Image super-resolution is a well-studied field that aims at generating high-resolution images from low-resolution inputs while preserving fine details and realistic features. Despite significant progress on regular images, inferring high-resolution textures of 3D models poses unique challenges. Due to the non-contiguous arrangement of texture patches, intended for wrapping around 3D meshes, applying conventional image super-resolution techniques to texture maps often results in artifacts and seams at texture discontinuities on the mesh. Additionally, obtaining ground truth data for texture super-resolution becomes highly complex due to the labor intensive process of hand-crafting ground truth textures for each mesh. We propose a generative deep learning network for texture map super-resolution using a differentiable renderer and calibrated reference images. Combining a super-resolution generative adversarial network (GAN) with differentiable rendering, we guide our network towards le arning realistic details and seamless texture map super-resolution without a high-resolution ground truth of the texture. Instead, we use high-resolution reference images. Through the differentiable rendering approach, we include model knowledge such as 3D meshes, projection matrices, and calibrated images to bridge the domain gap between 2D image super-resolution and texture map super-resolution. Our results show textures with fine structures and improved detail, which is especially of interest in virtual and augmented reality environments depicting humans. (More)

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Paper citation in several formats:
Bagdasarian, M.; Eisert, P. and Hilsmann, A. (2024). Generative Texture Super-Resolution via Differential Rendering. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 282-289. DOI: 10.5220/0012303300003660

@conference{visapp24,
author={Milena Bagdasarian. and Peter Eisert. and Anna Hilsmann.},
title={Generative Texture Super-Resolution via Differential Rendering},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={282-289},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012303300003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Generative Texture Super-Resolution via Differential Rendering
SN - 978-989-758-679-8
IS - 2184-4321
AU - Bagdasarian, M.
AU - Eisert, P.
AU - Hilsmann, A.
PY - 2024
SP - 282
EP - 289
DO - 10.5220/0012303300003660
PB - SciTePress