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Eunsu Kang 0001

dblp:90/1093 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0002-9738-9539ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Games and playful interaction · 62% Human-robot interaction · 19% Wearable and physiological sensing · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Games and playful interaction
interactive art
0.112006
Imago · ACM Multimedia 2006
Human-robot interaction › robot perception
human detection
0.012006
Imago · ACM Multimedia 2006
Wearable and physiological sensing › acoustic sensing
ultrasonic sensing
0.012006
Imago · ACM Multimedia 2006

Methods — techniques the papers use, named apart from their topics

ultrasonic sensing · 0.1
YearPublicationVenuePosition
2020 Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists
abstract
Robotic painting has been a subject of interest among both artists and roboticists since the 1970s. Researchers and interdisciplinary artists have employed various painting techniques and human-robot collaboration models to create visual mediums on canvas. One of the challenges of robotic painting is to apply a desired artistic style to the painting. Style transfer techniques with machine learning models have helped us address this challenge with the visual style of a specific painting. However, other manual elements of style, i.e., painting techniques and brushstrokes of an artist, have not been fully addressed.We propose a method to integrate an artistic style to the brushstrokes and the painting process through collaboration with a human artist. In this paper, we describe our approach to 1) collect brushstrokes and hand-brush motion samples from an artist, and 2) train a generative model to generate brushstrokes that pertains to the artist's style, and 3) fine tune a stroke-based rendering model to work with our robotic painting setup. We will report on the integration of these three steps in a separate publication. In a preliminary study, 71% of human evaluators find our reconstructed brushstrokes are pertaining to the characteristics of the artist's style. Moreover, 58% of participants could not distinguish a painting made by our method from a visually similar painting created by a human artist.
Ardavan Bidgoli, Manuel Ladron de Guevara, Cinnie Hsiung, Jean Oh, Eunsu Kang 0001
RO-MAN5
2006 Imago
abstract
Imago is an interactive system representing an "alien" that has a unique communication method of movie projection. Imago's body consists of projector turning, projection and human detection system using ultrasonic sensors. Imago projects its messages in the form of movies on the participant's body in response to their position and speed. Imago aims not to make a system simulating human gesture detection, but to represent the communication situation between two creatures having different/limited communication ways as artwork. Participants experience the Imago's method of communication and ruminate human communication requiring efforts to understand differences. Furthermore, Imago is expected to evoke image communication, which has been less emphasized since Gutenberg Galaxy [1], in the participant's mind.
Eunsu Kang 0001
ACM Multimedia1