Portrait Sketch Generative Model for Misaligned Photo-to-Sketch Dataset

Hyungbum Kim, Junho Kim and Heekyung Yang ()
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Hyungbum Kim: Department of Computer Science, Sangmyung University, Seoul 03016, Republic of Korea
Junho Kim: Department of Computer Science, Sangmyung University, Seoul 03016, Republic of Korea
Heekyung Yang: Department of Software, Sangmyung University, 31, Sangmyeongdae-gil, Dongnam-gu, Cheonan 31066, Republic of Korea

Mathematics, 2023, vol. 11, issue 17, 1-17

Abstract: A deep-learning-based model for generating line-based portrait sketches from portrait photos is proposed in this paper. The misalignment problem is addressed by the introduction of a novel loss term, designed to tolerate misalignments between Ground Truth sketches and generated sketches. Artists’ sketching strategies are mimicked by dividing the portrait into face and hair regions, with separate models trained for each region, and the outcomes subsequently combined. Our contributions include the resolution of misalignment between photos and artist-created sketches, and high-quality sketch results via region-based model training. The experimental results show the effectiveness of our approach in generating convincing portrait sketches, with both quantitative and visual comparisons to State-of-the-Art techniques. The quantitative comparisons demonstrate that our method preserves the identity of the input portrait photos, while applying the style of Ground Truth sketch.

Keywords: sketch; artist-created sketch; photo to sketch; portrait; GAN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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