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Recently, methods have been proposed that perform texture synthesis and styletransfer by using convolutional neural networks (e.g. Gatys et al.[2015,2016]). These methods are exciting because they can in some cases createresults with state-of-the-art quality. However, in this paper, we show thesemethods also have limitations in texture quality, stability, requisiteparameter tuning, and lack of user controls. This paper presents a multiscalesynthesis pipeline based on convolutional neural networks that amelioratesthese issues. We first give a mathematical explanation of the source ofinstabilities in many previous approaches. We then improve these instabilitiesby using histogram losses to synthesize textures that better statisticallymatch the exemplar. We also show how to integrate localized style losses in ourmultiscale framework. These losses can improve the quality of large features,improve the separation of content and style, and offer artistic controls suchas paint by numbers. We demonstrate that our approach offers improved quality,convergence in fewer iterations, and more stability over the optimization.

Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses