This paper aims for a new generation task: non-stationary multi-texture synthesis, which unifies synthesizing multiple non-stationary textures in a single model. Most non-stationary textures have large scale variance and can hardly be synthesized through one model. To combat this, we propose a multi-scale generator to capture structural patterns of various scales and effectively synthesize textures with a minor cost. However, it is still hard to handle textures of different categories with different texture patterns. Therefore, we present a category-specific training strategy to focus on learning texture pattern of a specific domain. Interestingly, once trained, our model is able to produce multi-pattern generations with dynamic variations without the need to finetune the model for different styles. Moreover, an objective evaluation metric is designed for evaluating the quality of texture expansion and global structure consistency. To our knowledge, ours is the first scheme for this challenging task, including model, training, and evaluation. Experimental results demonstrate the proposed method achieves superior performance and time efficiency. The code will be available after the publication.

翻译：本文旨在实现一项新生成任务：非平稳多纹理合成，即通过单一模型统一合成多种非平稳纹理。大多数非平稳纹理具有较大的尺度变化，难以通过单一模型合成。为此，我们提出一种多尺度生成器，以捕获不同尺度的结构模式，并以较低成本有效合成纹理。然而，处理不同类别的纹理及其差异模式仍具挑战性。因此，我们提出类别特异性训练策略，专注于学习特定域的纹理模式。有趣的是，模型一旦训练完成，无需针对不同风格进行微调，即可生成具有动态变化的多模式纹理。此外，我们设计了一项客观评估指标，用于衡量纹理扩展质量及全局结构一致性。据我们所知，本工作是针对该挑战性任务的首个解决方案，涵盖模型、训练与评估全流程。实验结果表明，所提方法在合成性能与时间效率上均表现优越。代码将在论文发表后公开。