Learning to Trace: Expressive Line Drawing Generation from Photographs
In this paper, we present a new computational method for automatically tracing high‐resolution photographs to create expressive line drawings. We define expressive lines as those that convey important edges, shape contours, and large‐scale texture lines that are necessary to accurately depict the ov...
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| Published in: | Computer graphics forum Vol. 38; no. 7; pp. 69 - 80 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Blackwell Publishing Ltd
01.10.2019
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | In this paper, we present a new computational method for automatically tracing high‐resolution photographs to create expressive line drawings. We define expressive lines as those that convey important edges, shape contours, and large‐scale texture lines that are necessary to accurately depict the overall structure of objects (similar to those found in technical drawings) while still being sparse and artistically pleasing. Given a photograph, our algorithm extracts expressive edges and creates a clean line drawing using a convolutional neural network (CNN). We employ an end‐to‐end trainable fully‐convolutional CNN to learn the model in a data‐driven manner. The model consists of two networks to cope with two sub‐tasks; extracting coarse lines and refining them to be more clean and expressive. To build a model that is optimal for each domain, we construct two new datasets for face/body and manga background. The experimental results qualitatively and quantitatively demonstrate the effectiveness of our model. We further illustrate two practical applications. |
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| AbstractList | In this paper, we present a new computational method for automatically tracing high‐resolution photographs to create expressive line drawings. We define expressive lines as those that convey important edges, shape contours, and large‐scale texture lines that are necessary to accurately depict the overall structure of objects (similar to those found in technical drawings) while still being sparse and artistically pleasing. Given a photograph, our algorithm extracts expressive edges and creates a clean line drawing using a convolutional neural network (CNN). We employ an end‐to‐end trainable fully‐convolutional CNN to learn the model in a data‐driven manner. The model consists of two networks to cope with two sub‐tasks; extracting coarse lines and refining them to be more clean and expressive. To build a model that is optimal for each domain, we construct two new datasets for face/body and manga background. The experimental results qualitatively and quantitatively demonstrate the effectiveness of our model. We further illustrate two practical applications. |
| Author | Yang, J. Price, B. Ito, D. Yamasaki, T. Inoue, N. Xu, N. |
| Author_xml | – sequence: 1 givenname: N. surname: Inoue fullname: Inoue, N. organization: The University of Tokyo – sequence: 2 givenname: D. surname: Ito fullname: Ito, D. organization: Adobe Research – sequence: 3 givenname: N. surname: Xu fullname: Xu, N. organization: Adobe Research – sequence: 4 givenname: J. surname: Yang fullname: Yang, J. organization: Adobe Research – sequence: 5 givenname: B. surname: Price fullname: Price, B. organization: Adobe Research – sequence: 6 givenname: T. surname: Yamasaki fullname: Yamasaki, T. organization: The University of Tokyo |
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| Cites_doi | 10.1109/CVPR.2016.28 10.1145/2897824.2925946 10.1111/cgf.12814 10.1109/CVPR.2018.00917 10.1145/2421636.2421640 10.1109/CVPR.2016.265 10.1145/1409060.1409108 10.1109/TPAMI.1986.4767851 10.1145/882262.882354 10.1109/ICCV.2013.231 10.1109/CVPR.2009.5206848 10.1007/s11263-018-1140-0 10.1145/1360612.1360687 10.1145/2897824.2925972 10.1007/978-3-642-33712-3_49 10.1007/s00371-018-1528-4 10.1145/3132703 10.1007/978-3-030-01231-1_35 10.1109/TPAMI.2004.1273918 10.1109/CVPR.2019.00162 10.1109/CVPR.2017.632 10.1109/CVPR.2005.39 10.1109/34.977560 10.1145/3197517.3201370 10.1109/TPAMI.2010.161 10.1109/WACV.2019.00154 10.1561/9781680835915 10.1109/ICCV.2015.164 |
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| Copyright | 2019 The Author(s) Computer Graphics Forum © 2019 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2019 The Eurographics Association and John Wiley & Sons Ltd. |
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| SubjectTerms | Algorithms Applied computing → Fine arts Artificial neural networks CCS Concepts Computing methodologies → Image manipulation Engineering drawings |
| Title | Learning to Trace: Expressive Line Drawing Generation from Photographs |
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