Joint Geometric-Semantic Driven Character Line Drawing Generation

TL;DR

This paper introduces P2LDGAN, a novel end-to-end generative adversarial network for automatic, high-quality character line drawing generation from photos, utilizing a joint geometric-semantic generator with cross-scale dense skip connections.

Contribution

The paper presents the first GAN-based architecture for character line drawing synthesis that incorporates geometric and semantic information through a novel generator design.

Findings

Outperforms state-of-the-art methods in quantitative metrics

Produces high-quality, diverse line drawings

Demonstrates effectiveness through extensive human evaluations

Abstract

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.