Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis. However, existing feed-forward based methods trade off generality for efficiency, which suffer from many issues, such as shortage of generality (i.e., build one network per texture), lack of diversity (i.e., always produce visually identical output) and suboptimality (i.e., generate less satisfying visual effects). In this work, we focus on solving these issues for improved texture synthesis. We propose a deep generative feed-forward network which enables efficient synthesis of multiple textures within one single network and meaningful interpolation between them. Meanwhile, a suite of important techniques are introduced to achieve better convergence and diversity. With extensive experiments, we demonstrate the effectiveness of the proposed model and techniques for synthesizing a large number of textures and show its applications with the stylization.
A concurrent related method recently proposed by @cite_14 handles multi-style transfer in one network by specializing scaling and shifting parameters after normalization to each specific texture. Our work differs from @cite_14 mainly in two aspects. First, we employ a different approach in representing textures. We represent textures as bits in a one-hot selection unit and as a continuous embedding vector within the network. Second, we propose diversity loss and incremental training scheme in order to achieve better convergence and output diverse results. Moreover, we demonstrate the effectiveness of our method on a much larger set of textures (e.g., 300) whereas @cite_14 develops a network for 32 textures.
@cite_14
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The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. We hope that this work provides a useful step towards building rich models of paintings and offers a window on to the structure of the learned representation of artistic style.