In this paper, we investigate the problem of automatically controllable artistic character line drawing generation from photographs by proposing a Vector Flow Aware and Line Controllable Image-to-Image Translation architecture, which can be viewed as an appealing intersection between Artificial Intelligence and Arts. Specifically, we first present an Image-to-Flow network (I2FNet) to efficiently and robustly create the vector flow field in a learning-based manner, which can provide a direction guide for drawing lines. Then, we introduce our well-designed Double Flow Generator (DFG) framework to fuse features from learned vector flow and input image flow guaranteeing the spatial coherence of lines. Meanwhile, in order to allow for controllable character line drawing generation, we integrate a Line Control Matrix (LCM) into DFG and train a Line Control Regressor (LCR) to synthesize drawings with different styles by elaborately controlling the level of details, such as thickness, smoothness, and continuity, of lines. Finally, we design a Fourier Transformation Loss to further constrain the character line generation from the frequency domain view of the point. Quantitative and qualitative experiments demonstrate that our approach can obtain superior performance in producing high-resolution character line-drawing images with perceptually realistic characteristics.

翻译：本文通过提出一种向量流感知且线条可控的图像到图像翻译架构，研究从照片自动生成艺术性人物线条画的可控问题，该架构可视为人工智能与艺术之间的有趣交叉。具体而言，我们首先提出图像到流网络（I2FNet），以学习方式高效鲁棒地创建向量流场，为绘制线条提供方向引导。接着，我们引入精心设计的双流生成器（DFG）框架，融合学习到的向量流与输入图像流的特征，保证线条的空间连贯性。同时，为实现可控的人物线条画生成，我们将线条控制矩阵（LCM）集成到DFG中，并训练线条控制回归器（LCR），通过精细控制线条的细节层级（如粗细、平滑度和连续性）合成不同风格的画作。最后，我们设计傅里叶变换损失，从频域视角进一步约束人物线条生成。定量与定性实验表明，我们的方法在生成具有感知真实特征的高分辨率人物线条画图像方面可获得优越性能。