User-Guided Chinese Painting Completion–A Generative Adversarial Network Approach

Image completion models based on deep neural networks have been a research hot spot in computer vision. However, most of the previous methods focus on natural images, such as faces and landscapes. In this paper, we propose a novel image completion model for a special set of artificial ancient Chinese paintings to address this limitation. Specifically, we integrate three complements: the Wasserstein Generative Adversarial Networks (WGAN), Perceptual loss, and Mean Squared Error (MSE) to train the model robustly. We propose a unique generator which can not only pay more attention to complete the details of ancient Chinese paintings but also can provide the synthesized lines to help artists to analyze paintings conveniently. Additionally, we also allow a user to supply a structure hint to guide our model to complete Chinese paintings according to his/her preference. Extensive experiments firmly demonstrate the effectiveness of our approach to complete ancient Chinese paintings and remove abnormal color blocks from them.