  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.
