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Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds

In recent years, the field of robotic portrait drawing has garnered considerable interest, as evidenced by the growing number of researchers focusing on either the speed or quality of the output drawing. However, the pursuit of either speed or quality alone has resulted in a trade-off between the tw...

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Autores principales: Nasrat, Shady, Kang, Taewoong, Park, Jinwoo, Kim, Joonyoung, Yi, Seung-Joon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10300977/
https://www.ncbi.nlm.nih.gov/pubmed/37420755
http://dx.doi.org/10.3390/s23125589
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author Nasrat, Shady
Kang, Taewoong
Park, Jinwoo
Kim, Joonyoung
Yi, Seung-Joon
author_facet Nasrat, Shady
Kang, Taewoong
Park, Jinwoo
Kim, Joonyoung
Yi, Seung-Joon
author_sort Nasrat, Shady
collection PubMed
description In recent years, the field of robotic portrait drawing has garnered considerable interest, as evidenced by the growing number of researchers focusing on either the speed or quality of the output drawing. However, the pursuit of either speed or quality alone has resulted in a trade-off between the two objectives. Therefore, in this paper, we propose a new approach that combines both objectives by leveraging advanced machine learning techniques and a variable line width Chinese calligraphy pen. Our proposed system emulates the human drawing process, which entails planning the sketch and creating it on the canvas, thus providing a realistic and high-quality output. One of the main challenges in portrait drawing is preserving the facial features, such as the eyes, mouth, nose, and hair, which are crucial for capturing the essence of a person. To overcome this challenge, we employ CycleGAN, a powerful technique that retains important facial details while transferring the visualized sketch onto the canvas. Moreover, we introduce the Drawing Motion Generation and Robot Motion Control Modules to transfer the visualized sketch onto a physical canvas. These modules enable our system to create high-quality portraits within seconds, surpassing existing methods in terms of both time efficiency and detail quality. Our proposed system was evaluated through extensive real-life experiments and showcased at the RoboWorld 2022 exhibition. During the exhibition, our system drew portraits of more than 40 visitors, yielding a survey outcome with a satisfaction rate of 95%. This result indicates the effectiveness of our approach in creating high-quality portraits that are not only visually pleasing but also accurate.
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spelling pubmed-103009772023-06-29 Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds Nasrat, Shady Kang, Taewoong Park, Jinwoo Kim, Joonyoung Yi, Seung-Joon Sensors (Basel) Article In recent years, the field of robotic portrait drawing has garnered considerable interest, as evidenced by the growing number of researchers focusing on either the speed or quality of the output drawing. However, the pursuit of either speed or quality alone has resulted in a trade-off between the two objectives. Therefore, in this paper, we propose a new approach that combines both objectives by leveraging advanced machine learning techniques and a variable line width Chinese calligraphy pen. Our proposed system emulates the human drawing process, which entails planning the sketch and creating it on the canvas, thus providing a realistic and high-quality output. One of the main challenges in portrait drawing is preserving the facial features, such as the eyes, mouth, nose, and hair, which are crucial for capturing the essence of a person. To overcome this challenge, we employ CycleGAN, a powerful technique that retains important facial details while transferring the visualized sketch onto the canvas. Moreover, we introduce the Drawing Motion Generation and Robot Motion Control Modules to transfer the visualized sketch onto a physical canvas. These modules enable our system to create high-quality portraits within seconds, surpassing existing methods in terms of both time efficiency and detail quality. Our proposed system was evaluated through extensive real-life experiments and showcased at the RoboWorld 2022 exhibition. During the exhibition, our system drew portraits of more than 40 visitors, yielding a survey outcome with a satisfaction rate of 95%. This result indicates the effectiveness of our approach in creating high-quality portraits that are not only visually pleasing but also accurate. MDPI 2023-06-14 /pmc/articles/PMC10300977/ /pubmed/37420755 http://dx.doi.org/10.3390/s23125589 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nasrat, Shady
Kang, Taewoong
Park, Jinwoo
Kim, Joonyoung
Yi, Seung-Joon
Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds
title Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds
title_full Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds
title_fullStr Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds
title_full_unstemmed Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds
title_short Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds
title_sort artistic robotic arm: drawing portraits on physical canvas under 80 seconds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10300977/
https://www.ncbi.nlm.nih.gov/pubmed/37420755
http://dx.doi.org/10.3390/s23125589
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AT kimjoonyoung artisticroboticarmdrawingportraitsonphysicalcanvasunder80seconds
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