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。该方法依赖于一个紧凑的编码器来捕获二阶神经统计特征,以及一个集成自适应周期性内容的生成器。图像被嵌入到一个紧凑且几何一致的潜在空间中,其中纹理表示与其空间组织被解耦。纹理合成和插值任务可直接通过这些潜在编码完成。实验表明,在视觉质量和多种纹理相关指标方面,我们的模型优于当前最先进的前馈方法。