Texture Representation via Analysis and Synthesis with Generative Adversarial Networks

• URL: http://arxiv.org/abs/2212.09983v1
• Date: Tue, 20 Dec 2022 03:57:11 GMT
• Title: Texture Representation via Analysis and Synthesis with Generative Adversarial Networks
• Authors: Jue Lin, Gaurav Sharma, Thrasyvoulos N. Pappas
• Abstract summary: We investigate data-driven texture modeling via analysis and synthesis with generative synthesis. We adopt StyleGAN3 for synthesis and demonstrate that it produces diverse textures beyond those represented in the training data. For texture analysis, we propose GAN using a novel latent consistency criterion for synthesized textures, and iterative refinement with Gramian loss for real textures.
• Score: 11.67779950826776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate data-driven texture modeling via analysis and synthesis with generative adversarial networks. For network training and testing, we have compiled a diverse set of spatially homogeneous textures, ranging from stochastic to regular. We adopt StyleGAN3 for synthesis and demonstrate that it produces diverse textures beyond those represented in the training data. For texture analysis, we propose GAN inversion using a novel latent domain reconstruction consistency criterion for synthesized textures, and iterative refinement with Gramian loss for real textures. We propose perceptual procedures for evaluating network capabilities, exploring the global and local behavior of latent space trajectories, and comparing with existing texture analysis-synthesis techniques.

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