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 (\textbf{D}raw Modern Chinese \textbf{L}andscape \textbf{P}hotos with \textbf{G}enerative \textbf{A}dversarial \textbf{N}etwork), 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.

提出了DLP-GAN，一种基于不对称循环映射的跨域图像转换框架，引入了一个基于密集融合模块的生成器以匹配不同的转换方向，并提出了一种双一致性损失用于平衡模型画作的真实感和抽象性，实现了现代意义上的山水照片和素描绘制。通过与其他基准方法比较，基于我们的现代山水和素描数据集，进行了大量实验证明我们的模型优于现有方法。

DLP-GAN：利用生成对抗网络学习绘制现代中国山水照片