  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.
