Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Mesh texture synthesis is a key component in the automatic generation of 3D content. Existing learning-based methods have drawbacks-either by disregarding the shape manifold during texture generation or by requiring a large number of different views to mitigate occlusion-related inconsistencies. In this paper, we present a novel surface-aware approach for mesh texture synthesis that overcomes these drawbacks by leveraging the pre-trained weights of 2D Convolutional Neural Networks (CNNs) with the same architecture, but with convolutions designed for 3D meshes. Our proposed network keeps track of the oriented patches surrounding each texel, enabling seamless texture synthesis and retaining local similarity to classical 2D convolutions with square kernels. Our approach allows us to synthesize textures that account for the geometric content of mesh surfaces, eliminating discontinuities and achieving comparable quality to 2D image synthesis algorithms. We compare our approach with state-of-the-art methods where, through qualitative and quantitative evaluations, we demonstrate that our approach is more effective for a variety of meshes and styles, while also producing visually appealing and consistent textures on meshes.
Description
CCS Concepts: Computing methodologies -> Neural networks; Texturing
@article{10.1111:cgf.15016,
journal = {Computer Graphics Forum},
title = {{Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs}},
author = {Kovács, Áron Samuel and Hermosilla, Pedro and Raidou, Renata Georgia},
year = {2024},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.15016}
}