Deep Neural Networks for Chinese Traditional Landscape Painting Creation

Document Type

Conference Proceeding

Publication Date



Deep learning techniques have been popularly applied for artistic tasks such as turning photographs into paintings or creating paintings in the style of modern art. However, East Asian arts are largely ignored. In this paper, we aim to apply deep learning models to create Chinese traditional landscape paintings. We achieve the goal through two deep learning techniques: image style transfer and image synthesis. We apply the Visual Geometry Group (VGG) Network to do the style transfer, which is trained on a pair of a content image and a style image, and the goal is to output an image that renders the target content with the desired style. For the image synthesis, we apply the Deep Convolutional Generative Adversarial Network (DCGAN), which requires a large set of painting images to produce as realistic as possible non-exist paintings that mimic the training dataset.

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering



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