NeRF-Texture: Texture synthesis with neural radiance fields

Huang, Yi-Hua, Cao, Yan-Pei, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680, Shan, Ying and Lin, Gao 2023. NeRF-Texture: Texture synthesis with neural radiance fields. Presented at: ACM SIGGRAPH, Los Angeles, CA, USA, 6-10 August 2023. ACM SIGGRAPH 2023 Conference Proceedings. Association for Computing Machinery, p. 43. 10.1145/3588432.3591484

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Abstract

Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance. Using this implicit representation, we can synthesize NeRF-based textures through patch matching of latent features. However, inconsistencies between the metrics of the reconstructed content space and the latent feature space may compromise the synthesis quality. To enhance matching performance, we further regularize the distribution of latent features by incorporating a clustering constraint. Experimental results and evaluations demonstrate the effectiveness of our approach.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Association for Computing Machinery
ISBN:	9798400701597
Funders:	The Royal Society
Date of First Compliant Deposit:	13 May 2023
Date of Acceptance:	20 April 2023
Last Modified:	19 Mar 2025 14:29
URI:	https://orca.cardiff.ac.uk/id/eprint/159467

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