  Chinese landscape painting has a unique and artistic style, and its drawing
technique is highly abstract in both the use of color and the realistic
representation of objects. Previous methods focus on transferring from modern
photos to ancient ink paintings. However, little attention has been paid to
translating landscape paintings into modern photos. To solve such problems, in
this paper, we (1) propose DLP-GAN (Draw Modern Chinese Landscape Photos with
Generative Adversarial Network), an unsupervised cross-domain image translation
framework with a novel asymmetric cycle mapping, and (2) introduce a generator
based on a dense-fusion module to match different translation directions.
Moreover, a dual-consistency loss is proposed to balance the realism and
abstraction of model painting. In this way, our model can draw landscape photos
and sketches in the modern sense. Finally, based on our collection of modern
landscape and sketch datasets, we compare the images generated by our model
with other benchmarks. Extensive experiments including user studies show that
our model outperforms state-of-the-art methods.
