用文本和边界条件生成多样且可无缝拼接的纹理图像。
Content-aware Tile Generation using Exterior Boundary Inpainting
- 通过边界约束将贴图生成转为图像修复问题，直接复用现有扩散模型。
- 仅凭文本提示即可生成多样化、可拼接的纹理，支持王氏贴图等方案。
- 提出新型双王氏拼接方案，提升纹理连续性与多样性，无需额外训练。
我们提出一种新颖且灵活的学习方法，用于生成可拼接的图像集。该方法超越简单的自拼接，支持高度多样化的相互可拼接图像集合。为促进多样性，我们解耦结构与内容，不显式复制示例图像的块。相反，利用大规模预训练扩散模型中嵌入的自然图像与纹理先验，结合外部边界条件和文本提示来引导贴图生成。通过精心设计和选择外部边界条件，我们将贴图生成过程重构为一个图像修复问题，从而可直接使用现有的基于扩散的修复模型，无需在自定义数据集上重新训练。我们在多种拼接方案（如王氏贴图）上展示了该方法的灵活性与有效性，仅需文本提示即可实现。此外，我们引入了一种新型双王氏拼接方案，相比现有变体提供了更高的纹理连续性和多样性。
原文摘要 · Abstract (English)
We present a novel and flexible learning-based method for generating tileable image sets. Our method goes beyond simple self-tiling, supporting sets of mutually tileable images that exhibit a high degree of diversity. To promote diversity we decouple structure from content by foregoing explicit copying of patches from an exemplar image. Instead we leverage the prior knowledge of natural images and textures embedded in large-scale pretrained diffusion models to guide tile generation constrained by exterior boundary conditions and a text prompt to specify the content. By carefully designing and selecting the exterior boundary conditions, we can reformulate the tile generation process as an inpainting problem, allowing us to directly employ existing diffusion-based inpainting models without the need to retrain a model on a custom training set. We demonstrate the flexibility and efficacy of our content-aware tile generation method on different tiling schemes, such as Wang tiles, from only a text prompt. Furthermore, we introduce a novel Dual Wang tiling scheme that provides greater texture continuity and diversity than existing Wang tile variants.
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