Texturing is a fundamental process in computer graphics. Texture is leveraged to enhance the visualization outcome for a 3D scene. In many cases a texture image cannot cover a large 3D model surface because of its small resolution. Conventional techniques like repeating, mirror repeating or clamp to edge do not yield visually acceptable results. Deep learning based texture synthesis has proven to be very effective in such cases. All deep texture synthesis methods trying to create larger resolution textures are limited in terms of GPU memory resources. In this paper, we propose a novel approach to example-based texture synthesis by using a robust deep learning process for creating tiles of arbitrary resolutions that resemble the structural components of an input texture. In this manner, our method is firstly much less memory limited owing to the fact that a new texture tile of small size is synthesized and merged with the original texture and secondly can easily produce missing parts of a large texture.

翻译：在计算机图形中, 纹理是一种基本过程。 纹理被利用来增强三维场景的可视化结果。 在许多情况下, 纹理图像由于分辨率小, 无法覆盖大型三维模型表面。 重复、 镜重复或夹在边缘等常规技术不会产生可视效果。 深学习的纹理合成在这类情况下证明非常有效。 所有试图创建更大分辨率纹理的深质合成方法在 GPU 内存资源方面都是有限的。 在本文中, 我们提出一种新的方法, 以实例为基础的纹理合成方法, 方法是利用强有力的深层次学习过程来创建任意分辨率的砖块, 类似输入纹理的结构组件 。 这样, 我们的方法首先会少得多的记忆力, 因为一个新的小纹理板被合成, 并且与原始纹理合并, 其次很容易产生大纹理缺失的部分 。