Existing deep-network based texture synthesis approaches all focus on fine-grained control of texture generation by synthesizing images from exemplars. Since the networks employed by most of these methods are always tied to individual exemplar textures, a large number of individual networks have to be trained when modeling various textures. In this paper, we propose to generate textures directly from coarse-grained control or high-level guidance, such as texture categories, perceptual attributes and semantic descriptions. We fulfill the task by parsing the generation process of a texture into the three-level Bayesian hierarchical model. A coarse-grained signal first determines a distribution over Markov random fields. Then a Markov random field is used to model the distribution of the final output textures. Finally, an output texture is generated from the sampled Markov random field distribution. At the bottom level of the Bayesian hierarchy, the isotropic and ergodic characteristics of the textures favor a construction that consists of a fully convolutional network. The proposed method integrates texture creation and texture synthesis into one pipeline for real-time texture generation, and enables users to readily obtain diverse textures with arbitrary scales from high-level guidance only. Extensive experiments demonstrate that the proposed method is capable of generating plausible textures that are faithful to user-defined control, and achieving impressive texture metamorphosis by interpolation in the learned texture manifold.

中文翻译：

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从粗粒度控制生成任意尺度纹理


现有的基于深度网络的纹理合成方法都侧重于通过合成样本图像来细粒度控制纹理生成。由于大多数这些方法所使用的网络总是与单独的示例纹理相关联，因此在对各种纹理进行建模时必须训练大量的单独网络。在本文中，我们建议直接从粗粒度控制或高级指导（例如纹理类别、感知属性和语义描述）生成纹理。我们通过将纹理的生成过程解析为三级贝叶斯分层模型来完成任务。粗粒度信号首先确定马尔可夫随机场上的分布。然后使用马尔可夫随机场对最终输出纹理的分布进行建模。最后，从采样的马尔可夫随机场分布生成输出纹理。在贝叶斯层次结构的底层，纹理的各向同性和遍历特性有利于由全卷积网络组成的结构。所提出的方法将纹理创建和纹理合成集成到一个管道中以进行实时纹理生成，并且使用户能够仅从高级指导中轻松获得任意尺度的各种纹理。大量的实验表明，所提出的方法能够生成忠实于用户定义控制的合理纹理，并通过在学习的纹理流形中进行插值来实现令人印象深刻的纹理变形。