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Recent progresses on deep discriminative and generative modeling have shownpromising results on texture synthesis. However, existing feed-forward basedmethods trade off generality for efficiency, which suffer from many issues,such as shortage of generality (i.e., build one network per texture), lack ofdiversity (i.e., always produce visually identical output) and suboptimality(i.e., generate less satisfying visual effects). In this work, we focus onsolving these issues for improved texture synthesis. We propose a deepgenerative feed-forward network which enables efficient synthesis of multipletextures within one single network and meaningful interpolation between them.Meanwhile, a suite of important techniques are introduced to achieve betterconvergence and diversity. With extensive experiments, we demonstrate theeffectiveness of the proposed model and techniques for synthesizing a largenumber of textures and show its applications with the stylization.

Diversified Texture Synthesis with Feed-forward Networks