We propose an auto-encoder architecture for multi-texture synthesis. The approach relies on both a compact encoder accounting for second order neural statistics and a generator incorporating adaptive periodic content. Images are embedded in a compact and geometrically consistent latent space, where the texture representation and its spatial organisation are disentangled. Texture synthesis and interpolation tasks can be performed directly from these latent codes. Our experiments demonstrate that our model outperforms state-of-the-art feed-forward methods in terms of visual quality and various texture related metrics.

翻译：我们提出了一种用于多纹理合成的自编码器架构。该方法同时利用了一个紧凑型编码器（考虑二阶神经统计量）和一个包含自适应周期内容的生成器。图像被嵌入到一个紧凑且几何一致的潜空间中，其中纹理表征与其空间组织被解耦。纹理合成与插值任务可直接基于这些潜在编码执行。实验结果表明，在视觉质量及多种纹理相关指标上，我们的模型优于当前最先进的前馈式方法。