Exemplar based regular texture synthesis using LSTM

Fast Texture Synthesis for Discrete Example-Based Elements

“…Following [25], the authors propose a global optimization algorithm to generate large building models based on small ones [26], and a tile-based method is proposed in [27] to generate facade building images. With the advent of neural networks, a method based on the long-short term memory (LSTM) that stems from Recurrent Neural Network (RNN) is proposed to produce regular texture [28], a feedforward network (FFN) is used to synthesize diverse texture by interpolating texture images generated by different FNN layers and maximizing stylized texture quality [29], [30], and the authors in [31] propose a conditional generative CNN-based (CGCNN) algorithm for synthesizing examplebased texture with non-local structures. Moreover, the research in [1] designs a novel method for growing texture over a 3D surface, and a camera-aided texturing method is proposed by integrating artistic tools into the texture synthesis system [32].…”

“…Final, we use the CNN-based texture transfer technology to stylize the optimized texture images. Compared with some existing methods (such as [18], [20] and [28]) for texturing that may produce some image seams and just optimize the local texture distribution (see Fig. 12), our method starts with preserving the integrity of elements in the exemplar, then optimizes the elements distribution globally, and can save more time for texturing process.…”