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.