Chinese Painting Generation With a Stroke-By-Stroke Renderer and a Semantic Loss

Abstract

Chinese painting is the traditional way of painting in China, with distinctive artistic characteristics and a strong national style. Creating Chinese paintings is a complex and difficult process for non-experts, so utilizing computer-aided Chinese painting generation is a meaningful topic. In this paper, we propose a novel Chinese painting generation model, which can generate vivid Chinese paintings in a stroke-by-stroke manner. In contrast to previous neural renderers, we design a Chinese painting renderer that can generate two classic stroke types of Chinese painting (i.e., middle-tip stroke and side-tip stroke), without the aid of any neural network. To capture the subtle semantic representation from the input image, we design a semantic loss to compute the distance between the input image and the output Chinese painting. Experiments demonstrate that our method can generate vivid and elegant Chinese paintings.