research-article

Eye movement synthesis with 1/f pink noise

Authors:

Andrew Duchowski,

Krzysztof KrejtzAuthors Info & Claims

MIG '15: Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games

Pages 47 - 56

https://doi.org/10.1145/2822013.2822014

Published: 16 November 2015 Publication History

Abstract

Eye movements are an essential part of non-verbal behavior. Non-player characters (NPCs), as they occur in many games, communicate with the player through dialogues and non-verbal behavior and can have a strong influence on the player experience or even on gameplay. In this paper we propose a procedural model to synthesize the subtleties of eye motions. More specifically, our model adds microsaccadic jitter and pupil unrest both modeled by 1/f or pink noise to the standard main sequence. In a perceptual two-alternative forced-choice (2AFC) experiment we explore the perceived naturalness of different parameters of pink noise by comparing synthesized motions to rendered motion of recorded eye movements at extreme close shot and close shot distances. Our results show that, on average, data-driven motion is perceived as most natural, followed by parameterized pink noise, with motion lacking microsaccadic jitter being consistently selected as the least natural in appearance.

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Cited By

Pan YAgrawal RSingh K(2024)S3: Speech, Script and Scene driven Head and Eye AnimationACM Transactions on Graphics10.1145/365817243:4(1-12)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658172
Canales RJain EJörg S(2023)Real-Time Conversational Gaze Synthesis for AvatarsProceedings of the 16th ACM SIGGRAPH Conference on Motion, Interaction and Games10.1145/3623264.3624446(1-7)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3623264.3624446
Prasse PReich DMakowski SAhn SScheffer TJäger L(2023)SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial NetworksProceedings of the 2023 Symposium on Eye Tracking Research and Applications10.1145/3588015.3588410(1-9)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588015.3588410
Show More Cited By

Index Terms

Eye movement synthesis with 1/f pink noise
1. Computing methodologies
  1. Computer graphics
    1. Animation

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MIG '15: Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games

November 2015

247 pages

ISBN:9781450339919

DOI:10.1145/2822013

Copyright © 2015 ACM.

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Published: 16 November 2015

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National Science Foundation

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MIG '15

MIG '15: Motion in Games

November 16 - 18, 2015

Paris, France

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Overall Acceptance Rate -9 of -9 submissions, 100%

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Cited By

Pan YAgrawal RSingh K(2024)S3: Speech, Script and Scene driven Head and Eye AnimationACM Transactions on Graphics10.1145/365817243:4(1-12)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658172
Canales RJain EJörg S(2023)Real-Time Conversational Gaze Synthesis for AvatarsProceedings of the 16th ACM SIGGRAPH Conference on Motion, Interaction and Games10.1145/3623264.3624446(1-7)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3623264.3624446
Prasse PReich DMakowski SAhn SScheffer TJäger L(2023)SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial NetworksProceedings of the 2023 Symposium on Eye Tracking Research and Applications10.1145/3588015.3588410(1-9)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588015.3588410
Fuhl WKasneci E(2022)HPCGen: Hierarchical K-Means Clustering and Level Based Principal Components for Scan Path Genaration2022 Symposium on Eye Tracking Research and Applications10.1145/3517031.3529625(1-7)Online publication date: 8-Jun-2022
https://dl.acm.org/doi/10.1145/3517031.3529625
Lan GScargill TGorlatova M(2022)EyeSyn: Psychology-inspired Eye Movement Synthesis for Gaze-based Activity Recognition2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN54338.2022.00026(233-246)Online publication date: May-2022
https://doi.org/10.1109/IPSN54338.2022.00026
Jayawardana YJayawardena GDuchowski AJayarathna SHealy PBilauca MBonnici A(2021)Metadata-driven eye tracking for real-time applicationsProceedings of the 21st ACM Symposium on Document Engineering10.1145/3469096.3474935(1-4)Online publication date: 16-Aug-2021
https://dl.acm.org/doi/10.1145/3469096.3474935
Fuhl WRong YKasneci E(2021)Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9413268(142-149)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9413268
Kar A(2020)MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking SystemsVision10.3390/vision40200254:2(25)Online publication date: 7-May-2020
https://doi.org/10.3390/vision4020025
Jörg SDuchowski AKrejtz KNiedzielska A(2019)Perceptual Comparison of Procedural and Data-Driven Eye Motion JitterACM Symposium on Applied Perception 201910.1145/3343036.3343130(1-5)Online publication date: 19-Sep-2019
https://dl.acm.org/doi/10.1145/3343036.3343130
Duchowski AJörg SScrews JGehrer NSchönenberg MKrejtz KKrejtz KSharif B(2019)Guiding gazeProceedings of the 11th ACM Symposium on Eye Tracking Research & Applications10.1145/3314111.3319848(1-9)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3314111.3319848
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