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20172023
most citedTransforming Exploratory Creativity with DeLeNoX

52 citations · 240 across the 22 of their papers we have counts for

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Showing cs.HCShow all

7 papers · 1 filter

cs.HC2021

"What Artists Want": Elicitation of Artist Requirements to Feed the Design on a New Collaboration Platform for Creative Work

Angeliki Antoniou, Ioanna Lykourentzou, Antonios Liapis +2

Aiming at designing a decentralized platform to support grassroot initiatives for self-organized creative work, the present work solicited feedback from a group of visual artists r…

cs.HC2021

Towards General Models of Player Experience: A Study Within Genres

David Melhart, Antonios Liapis, Georgios N. Yannakakis

To which degree can abstract gameplay metrics capture the player experience in a general fashion within a game genre? In this comprehensive study we address this question across th…

cs.HC2021

Privileged Information for Modeling Affect In The Wild

Konstantinos Makantasis, David Melhart, Antonios Liapis +1

A key challenge of affective computing research is discovering ways to reliably transfer affect models that are built in the laboratory to real world settings, namely in the wild.…

cs.HC2021★ 50 cited

The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games

Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis

What if emotion could be captured in a general and subject-agnostic fashion? Is it possible, for instance, to design general-purpose representations that detect affect solely from…

cs.HC2019

From Pixels to Affect: A Study on Games and Player Experience

Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis

Is it possible to predict the affect of a user just by observing her behavioral interaction through a video? How can we, for instance, predict a user's arousal in games by merely l…

cs.HC2019

PAGAN: Video Affect Annotation Made Easy

David Melhart, Antonios Liapis, Georgios N. Yannakakis

How could we gather affect annotations in a rapid, unobtrusive, and accessible fashion? How could we still make sure that these annotations are reliable enough for data-hungry affe…