In this paper, we investigate deep image synthesis guided by sketch, color, and texture. Previous image synthesis methods can be controlled by sketch and color strokes but we are the first to examine texture control. We allow a user to place a texture patch on a sketch at arbitrary location and scale to control the desired output texture. Our generative network learns to synthesize objects consistent with these texture suggestions. To achieve this, we develop a local texture loss in addition to adversarial and content loss to train the generative network. The new local texture loss can improve generated texture quality without knowing the patch location and size in advance. We conduct experiments using sketches generated from real images and textures sampled from the Describable Textures Dataset and results show that our proposed algorithm is able to generate plausible images that are faithful to user controls. Ablation studies show that our proposed pipeline can generate more realistic images than adapting existing methods directly.

本文介绍了一种通过草图、颜色和纹理生成深层次图像的方法。通过引入纹理控制来实现用户对输出纹理的控制，我们的生成网络可以学习到如何根据用户的纹理想法生成与之一致的对象，并且在实验中得到了验证。

TextureGAN：使用纹理贴片控制深度图像合成