  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.
