Multiple research studies have recently demonstrated deep networks can generate realistic-looking textures and stylised images from a single texture example. However, they suffer from some drawbacks. Generative adversarial networks are in general difficult to train. Multiple feature variations, encoded in their latent representation, require a priori information to generate images with specific features. The auto-encoders are prone to generate a blurry output. One of the main reasons is the inability to parameterise complex distributions. The authors present a novel texture generative model architecture extending the variational auto-encoder approach. It gradually increases the accuracy of details in the reconstructed images. Thanks to the proposed architecture, the model is able to learn a higher level of details resulting from the partial disentanglement of latent variables. The generative model is also capable of synthesising complex real-world textures. The model consists of multiple separate latent layers responsible for learning the gradual levels of texture details. Separate training of latent representations increases the stability of the learning process and provides partial disentanglement of latent variables. The experiments with proposed architecture demonstrate the potential of variational auto-encoders in the domain of texture synthesis and also tend to yield sharper reconstruction as well as synthesised texture images.

中文翻译：

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用于纹理合成的分层变分自动编码器的部分解缠

最近的多项研究表明，深层网络可以从单个纹理示例生成逼真的纹理和风格化的图像。但是，它们具有一些缺点。生成对抗网络通常很难训练。以其潜在表示进行编码的多个特征变体需要先验信息以生成具有特定特征的图像。自动编码器易于产生模糊的输出。主要原因之一是无法参数化复杂分布。作者提出了一种新颖的纹理生成模型架构，该模型扩展了变分自动编码器方法。它逐渐提高了重建图像中细节的准确性。由于拟议的架构，该模型能够学习由于潜在变量的部分解缠而产生的更高级别的细节。生成模型还能够合成复杂的真实世界纹理。该模型由多个单独的潜在层组成，负责学习纹理细节的逐渐级别。单独训练潜在表示可以提高学习过程的稳定性，并可以部分消除潜在变量的纠缠。提出的体系结构的实验证明了变分自动编码器在纹理合成领域的潜力，并且还倾向于产生更清晰的重建以及合成的纹理图像。该模型由多个单独的潜在层组成，负责学习纹理细节的逐渐级别。单独训练潜在表示可以提高学习过程的稳定性，并可以部分消除潜在变量的纠缠。提出的体系结构的实验证明了变分自动编码器在纹理合成领域的潜力，并且还倾向于产生更清晰的重建以及合成的纹理图像。该模型由多个单独的潜在层组成，负责学习纹理细节的逐渐级别。单独训练潜在表示可以提高学习过程的稳定性，并可以部分消除潜在变量的纠缠。提出的体系结构的实验证明了变分自动编码器在纹理合成领域的潜力，并且还倾向于产生更清晰的重建以及合成的纹理图像。