We present Infinite Texture, a method for generating arbitrarily large
texture images from a text prompt. Our approach fine-tunes a diffusion model on
a single texture, and learns to embed that statistical distribution in the
output domain of the model. We seed this fine-tuning process with a sample
texture patch, which can be optionally generated from a text-to-image model
like DALL-E 2. At generation time, our fine-tuned diffusion model is used
through a score aggregation strategy to generate output texture images of
arbitrary resolution on a single GPU. We compare synthesized textures from our
method to existing work in patch-based and deep learning texture synthesis
methods. We also showcase two applications of our generated textures in 3D
rendering and texture transfer.

从文本提示生成任意大小的纹理图像的新方法，通过精调扩散模型实现单一 GPU 上任意分辨率的输出纹理图像，并展示了生成纹理在 3D 渲染和纹理转换中的两个应用。

无限纹理：基于文本引导的高分辨率扩散纹理合成

Infinite Texture: Text-guided High Resolution Diffusion Texture  Synthesis

In this paper, we propose a new framework for improving Content Based Image
Retrieval (CBIR) for texture images. This is achieved by using a new image
representation based on the RCT-Plus transform which is a novel variant of the
Redundant Contourlet transform that extracts a richer directional information
in the image. Moreover, the process of image search is improved through a
learning-based approach where the images of the database are classified using
an adapted similarity metric to the statistical modeling of the RCT-Plus
transform. A query is then first classified to select the best texture class
after which the retained class images are ranked to select top ones. By this,
we have achieved significant improvements in the retrieval rates compared to
previous CBIR schemes.

基于 RCT-Plus 变换的内容基于图像检索（CBIR）框架通过提取图像中更丰富的方向信息的新形象表示，改进了纹理图像的图像检索过程；通过使用适应于 RCT-Plus 变换统计建模的相似度度量对数据库中的图像进行分类，然后对查询进行分类以选择最佳纹理类别，之后对保留类别的图像进行排序以选择最佳图像，从而实现了与以前的 CBIR 方案相比显著的检索率提高。

基于分类和 Contourlet 特征的纹理图像检索

Texture image retrieval using a classification and contourlet-based  features

In this paper, we introduce a novel approach for generating texture images of
infinite resolutions using Generative Adversarial Networks (GANs) based on a
patch-by-patch paradigm. Existing texture synthesis techniques often rely on
generating a large-scale texture using a one-forward pass to the generating
model, this limits the scalability and flexibility of the generated images. In
contrast, the proposed approach trains GANs models on a single texture image to
generate relatively small patches that are locally correlated and can be
seamlessly concatenated to form a larger image while using a constant GPU
memory footprint. Our method learns the local texture structure and is able to
generate arbitrary-size textures, while also maintaining coherence and
diversity. The proposed method relies on local padding in the generator to
ensure consistency between patches and utilizes spatial stochastic modulation
to allow for local variations and diversity within the large-scale image.
Experimental results demonstrate superior scalability compared to existing
approaches while maintaining visual coherence of generated textures.

本文介绍了一种使用基于生成对抗网络（GANs）的逐片段范例生成无限分辨率纹理图像的新方法。该方法通过在一个纹理图像上训练 GAN 模型，生成相对较小但局部相关且可无缝拼接成更大图像的纹理片段，并具有可扩展性、一致性和多样性。实验结果表明，相比于现有方法，该方法具有更好的可扩展性和视觉一致性。

使用基于逐块方式的 GAN 生成无限分辨率纹理

Generating Infinite-Resolution Texture using GANs with Patch-by-Patch  Paradigm

In this work, we focus on synthesizing high-quality textures on 3D meshes. We
present Point-UV diffusion, a coarse-to-fine pipeline that marries the
denoising diffusion model with UV mapping to generate 3D consistent and
high-quality texture images in UV space. We start with introducing a point
diffusion model to synthesize low-frequency texture components with our
tailored style guidance to tackle the biased color distribution. The derived
coarse texture offers global consistency and serves as a condition for the
subsequent UV diffusion stage, aiding in regularizing the model to generate a
3D consistent UV texture image. Then, a UV diffusion model with hybrid
conditions is developed to enhance the texture fidelity in the 2D UV space. Our
method can process meshes of any genus, generating diversified,
geometry-compatible, and high-fidelity textures. Code is available at
this https URL

通过 Point-UV 扩散模型和 UV 映射，在 3D 网格上合成高质量纹理图像，以生成 3D 一致且高质量的 UV 纹理图像。