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.

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写实表达方面极具抽象性。现有方法主要聚焦于现代照片向古代水墨画的风格迁移，然而将山水画转化为现代照片的研究却鲜有涉及。为解决这一问题，本文提出：（1）DLP-GAN（基于生成对抗网络绘制现代中国山水照片）——一种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。同时，提出双重一致性损失以平衡模型绘画的写实性与抽象性。通过上述方法，本模型可绘制具有现代感的山水照片与素描。最终，基于自建的现代山水与素描数据集，我们将模型生成图像与其他基准方法进行对比。涵盖用户调研在内的大量实验表明，本模型性能优于现有最先进方法。