组合式神经纹理
Compositional Neural Textures
AI总结：
提出一种无监督的组合式神经纹理表示方法，将纹理建模为2D Gaussian texton的离散组合，支持高效合成与多种编辑应用，推进了纹理分析与建模技术。
摘要：
纹理在增强真实照片和计算机生成图像的视觉丰富度方面起着至关重要的作用。然而，编辑纹理的过程通常涉及对texton（表征纹理的重复局部模式）进行繁琐且重复的手动调整。本研究引入了一种完全无监督的方法，使用组合式神经模型来表示纹理，该模型能够捕获单个texton。我们将每个texton表示为一个2D Gaussian函数，其空间支撑近似其形状，并使用相关联的特征来编码其详细外观。通过将纹理建模为Gaussian texton的离散组合，该表示既提供了表现力又易于编辑。可以通过修改潜在空间内的组合式Gaussian来编辑纹理，并且以前馈方式将修改后的Gaussian输入生成器网络，即可高效合成新纹理。该方法支持广泛的应用，包括将图像纹理的外观转移到另一图像、使纹理多样化、纹理插值、揭示/修改纹理变化、编辑传播、纹理动画以及直接操作texton。所提出的方法有助于推进纹理分析、建模和编辑技术，并为创建具有可控纹理的视觉吸引力图像开辟了新的可能性。
英文摘要：
Texture plays a vital role in enhancing visual richness in both real photographs and computer-generated imagery. However, the process of editing textures often involves laborious and repetitive manual adjustments of textons, which are the recurring local patterns that characterize textures. This work introduces a fully unsupervised approach for representing textures using a compositional neural model that captures individual textons. We represent each texton as a 2D Gaussian function whose spatial support approximates its shape, and an associated feature that encodes its detailed appearance. By modeling a texture as a discrete composition of Gaussian textons, the representation offers both expressiveness and ease of editing. Textures can be edited by modifying the compositional Gaussians within the latent space, and new textures can be efficiently synthesized by feeding the modified Gaussians through a generator network in a feed-forward manner. This approach enables a wide range of applications, including transferring appearance from an image texture to another image, diversifying textures,texture interpolation, revealing/modifying texture variations, edit propagation, texture animation, and direct texton manipulation. The proposed approach contributes to advancing texture analysis, modeling, and editing techniques, and opens up new possibilities for creating visually appealing images with controllable textures.
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