Joint Geometric-Semantic Driven Character Line Drawing Generation
| Autor: | Fang, Cheng-Yu, Han, Xian-Feng |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | In Proceedings of ICMR '23. Association for Computing Machinery, New York, NY, USA, 226-233 (2023) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3591106.3592216 |
| Popis: | 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. Comment: Published in ICMR '23: Proceedings of the 2023 ACM International Conference on Multimedia Retrieval, June 2023 |
| Databáze: | arXiv |
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