Automated image style transfer is of great interest given the recent advances in generative adversarial networks (GANs). However, it is challenging to generate synthesized images from abstract masks while preserving detailed patterns for certain kinds of art given small datasets. We propose an intelligent GAN-based system enhanced with user intent and prior knowledge for generating images styled as Cantonese porcelain using user-defined masks. Given a mask with specified objects, our system first generates a synthesized natural image. We then use a novel semantic user intent enhancement module to retrieve semantically relevant images from an image dataset. Objects in the retrieved image are used to refine local patterns in the synthesized image. Finally, the refined image is restyled in the Cantonese porcelain style. The system is trained by 454 pairs of natural images and semantic segmentation of 24 objects from the COCO dataset for synthesized image generation from masks, and 1445 Cantonese porcelain images for style transfer. Experimental results and ablation studies demonstrate that the synthesized and restyled images were improved with local details and enhanced contrast.

中文翻译：

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使用用户引导的生成对抗网络生成粤语瓷器图像

鉴于生成对抗网络 (GAN) 的最新进展，自动化图像风格转移引起了人们极大的兴趣。然而，在给定小数据集的情况下，在为某些类型的艺术保留详细模式的同时，从抽象掩码生成合成图像是具有挑战性的。我们提出了一种基于用户意图和先验知识的基于智能 GAN 的系统，用于使用用户定义的掩码生成具有粤式瓷器风格的图像。给定具有指定对象的掩码，我们的系统首先生成合成的自然图像。然后，我们使用一种新颖的语义用户意图增强模块从图像数据集中检索语义相关的图像。检索到的图像中的对象用于细化合成图像中的局部模式。最后，精致的形象被重新设计成粤式瓷器风格。该系统由 454 对自然图像和来自 COCO 数据集的 24 个对象的语义分割进行训练，用于从蒙版生成合成图像，以及 1445 幅粤语瓷器图像用于风格转换。实验结果和消融研究表明，合成和重新设计的图像通过局部细节和增强的对比度得到了改进。