Character line drawing synthesis can be formulated as a special case of image-to-image translation problem that automatically manipulates the photo-to-line drawing style transformation. In this paper, we present the first generative adversarial network based end-to-end trainable translation architecture, dubbed P2LDGAN, for automatic generation of high-quality character drawings from input photos/images. The core component of our approach is the joint geometric-semantic driven generator, which uses our well-designed cross-scale dense skip connections framework to embed learned geometric and semantic information for generating delicate line drawings. In order to support the evaluation of our model, we release a new dataset including 1,532 well-matched pairs of freehand character line drawings as well as corresponding character images/photos, where these line drawings with diverse styles are manually drawn by skilled artists. Extensive experiments on our introduced dataset demonstrate the superior performance of our proposed models against the state-of-the-art approaches in terms of quantitative, qualitative and human evaluations. Our code, models and dataset will be available at Github.

翻译：角色线条画合成可以视为一种特殊的图像到图像翻译问题，它自动实现照片到线条画风格的转换。本文提出首个基于生成对抗网络的端到端可训练翻译架构，命名为P2LDGAN，用于从输入照片/图像自动生成高质量角色线条画。我们方法的核心组件是联合几何-语义驱动生成器，该生成器采用精心设计的跨尺度密集跳跃连接框架，嵌入学习到的几何及语义信息以生成细腻线条画。为支持模型评估，我们发布了一个新数据集，包含1532对精心匹配的手绘角色线条画及其对应的角色图像/照片，这些具有多种风格的线条画由技艺娴熟的艺术家手工绘制。在我们引入的数据集上进行的广泛实验表明，所提模型在定量、定性和人工评估方面均优于当前最先进方法。我们的代码、模型及数据集将在Github上公开提供。