Painting style transfer is an attractive and challenging computer vision problem that aims to transfer painting styles onto natural images. Existing advanced methods tackle this problem from the perspective of Neural Style Transfer (NST) or unsupervised cross-domain image translation. For both two types of methods, attention has been focused on reproducing artistic painting styles of representative artists (e.g., Vincent Van Gogh). In this article, instead of transferring styles of artistic paintings, we focus on automatic generation of realistic paintings, for example, making the machine draw a gouache before a still life, paint a sketch of a landscape, or draw a pen-and-ink portrait of a person, etc. Besides capturing the precise target styles, synthesis of realistic paintings is more demanding in preserving original content features and image structures, for which existing advanced methods are not sufficient to generate satisfactory results. Aimed at this problem, we propose RPD-GAN (Realistic Painting Drawing Generative Adversarial Network), an unsupervised cross-domain image translation framework for realistic painting style transfer. At the heart of our model is the decomposition of the image stylization mapping into four stages: feature encoding, feature de-stylization, feature re-stylization, and feature decoding, where the functionalities of these stages are implemented by additionally embedding a content-consistency constraint and a style-alignment constraint at feature space to the classic CycleGAN architecture. By enforcing these constraints, both the content-preserving and style-capturing capabilities of the model are enhanced, leading to higher-quality stylization results. Extensive experiments demonstrate the effectiveness and superiority of our RPD-GAN in drawing realistic paintings.

中文翻译：

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RPD-GAN：学习使用生成对抗网络绘制写实绘画


绘画风格迁移是一个有吸引力且具有挑战性的计算机视觉问题，旨在将绘画风格迁移到自然图像上。现有的先进方法从神经风格迁移（NST）或无监督跨域图像翻译的角度解决了这个问题。对于这两种方法，注意力都集中在再现代表性艺术家（例如文森特·梵高）的艺术绘画风格上。在这篇文章中，我们不转移艺术画的风格，而是专注于自动生成写实画，例如让机器在静物前画水粉，画风景素描，或者画钢笔画写实绘画的合成除了捕捉精确的目标风格外，对保留原始内容特征和图像结构要求更高，现有的先进方法不足以产生令人满意的结果。针对这个问题，我们提出了RPD-GAN（写实绘画生成对抗网络），一种用于写实绘画风格迁移的无监督跨域图像翻译框架。我们模型的核心是将图像风格化映射分解为四个阶段：特征编码、特征去风格化、特征重新风格化和特征解码，其中这些阶段的功能是通过另外嵌入内容一致性来实现的经典 CycleGAN 架构的特征空间约束和样式对齐约束。通过实施这些约束，模型的内容保留和风格捕获能力都得到增强，从而产生更高质量的风格化结果。 大量的实验证明了我们的 RPD-GAN 在绘制写实绘画方面的有效性和优越性。