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Improving VIP viewer Gaze Estimation and Engagement Using Adaptive Dynamic Anamorphosis

Pan, Ye; Mitchell, Kenny


Ye Pan


Anamorphosis for 2D displays can provide viewer centric perspective viewing, enabling 3D appearance, eye contact and engagement, by adapting dynamically in real time to a single moving viewer’s viewpoint, but at the cost of distorted viewing for other viewers. We present a method for constructing non-linear projections as a combination of anamorphic rendering of selective objects whilst reverting to normal perspective rendering of the rest of the scene. Our study defines a scene consisting of five characters, with one of these characters selectively rendered in anamorphic perspective. We conducted an evaluation experiment and demonstrate that the tracked viewer-centric imagery for the selected character results in an improved gaze and engagement estimation. Critically, this is performed without sacrificing the other viewers’ viewing experience. In addition, we present findings on the perception of gaze direction for regularly viewed characters located off-center to the origin, where perceived gaze shifts from being aligned to misalignment increasingly as the distance between viewer and character increases. Finally, we discuss different viewpoints and the spatial relationship between objects.


Pan, Y., & Mitchell, K. (2021). Improving VIP viewer Gaze Estimation and Engagement Using Adaptive Dynamic Anamorphosis. International Journal of Human-Computer Studies, 147, Article 102563.

Journal Article Type Article
Acceptance Date Oct 25, 2020
Online Publication Date Nov 10, 2020
Publication Date 2021-03
Deposit Date Nov 11, 2020
Publicly Available Date Nov 11, 2021
Journal International Journal of Human-Computer Studies
Print ISSN 1071-5819
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 147
Article Number 102563
Keywords Gaze, Dynamic Anamorphosis, Display
Public URL
Publisher URL


Improving VIP Viewer Gaze Estimation And Engagement Using Adaptive Dynamic Anamorphosis (accepted version) (18.1 Mb)


Copyright Statement
Accepted version licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

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