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Structure Preserving Exemplar-Based 3D Texture Synthesis

In this paper we propose exemplar-based 3D texture synthesis method which unlike existing neural network approaches preserve structural elements in texture. The proposed approach does this by accounting additional image properties which stand for the preservation of the structure with the help of a specially constructed error function used for training neural networks. Thanks to the proposed solution we can apply 2D texture to any 3D model (even without texture coordinates) by synthesizing high quality 3D texture and using local or world space position of surface instead 2D texture coordinates (fig. 1). Our solution is based on introducing 3 different error components in to the process of neural network fitting which helps to preserve desired properties of generated texture. The first component is for structuredness of the generated texture and the sample, the second component increases the diversity of the generated textures and the third one prevents abrupt transitions between individual pixels.