“Learning to Generate Artistic Character Line Drawing”, 2022-06-07 ():
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.