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.

翻译：中国山水画具有独特的艺术风格，其绘画技法在色彩运用与物体具象表现方面呈现出高度抽象性。现有方法主要聚焦于将现代照片转换为古代水墨画，而鲜有研究涉及将山水画转化为现代照片。为解决该问题，本文：(1)提出DLP-GAN（基于生成对抗网络的现代中国山水照片绘制框架），这是一个采用新型非对称循环映射的无监督跨域图像翻译框架；(2)引入基于密集融合模块的生成器以适配不同翻译方向。此外，我们提出双重一致性损失函数来平衡模型绘画的真实性与抽象性，从而绘制具有现代感的山水照片与素描。最后，基于我们收录的现代山水与素描数据集，将模型生成的图像与现有基准方法进行对比。涵盖用户研究在内的大量实验表明，我们的模型性能优于当前最优方法。