RPD-GAN: Learning to Draw Realistic Paintings with Generative Adversarial Network.

Abstract

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 paper, 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.