A gaze-responsive self-disclosing display
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Life-Like Characters: Tools, Affective Functions, and Applications (Cognitive Technologies)
Life-Like Characters: Tools, Affective Functions, and Applications (Cognitive Technologies)
Conversing with the user based on eye-gaze patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
International Journal of Intelligent Systems - Uncertain Reasoning (Part 1)
Intelligent Interactive Entertainment Grand Challenges
IEEE Intelligent Systems
Gaze-based infotainment agents
Proceedings of the international conference on Advances in computer entertainment technology
MPML3D: a reactive framework for the multimodal presentation markup language
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
Dynamic Bayesian network based interest estimation for visual attentive presentation agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Multi-mode saliency dynamics model for analyzing gaze and attention
Proceedings of the Symposium on Eye Tracking Research and Applications
Semantic interpretation of eye movements using designed structures of displayed contents
Proceedings of the 4th Workshop on Eye Gaze in Intelligent Human Machine Interaction
Learning aspects of interest from Gaze
Proceedings of the 6th workshop on Eye gaze in intelligent human machine interaction: gaze in multimodal interaction
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In this paper, we describe an interface consisting of a virtual showroom where a team of two highly realistic 3D agents presents product items in an entertaining and attractive way. The presentation flow adapts to users' attentiveness, or lack thereof, and may thus provide a more personalized and user-attractive experience of the presentation. In order to infer users' attention and visual interest regarding interface objects, our system analyzes eye movements in real-time. Interest detection algorithms used in previous research determine an object of interest based on the time that eye gaze dwells on that object. However, this kind of algorithm is not well suited for dynamic presentations where the goal is to assess the user's focus of attention regarding a dynamically changing presentation. Here, the current context of the object of attention has to be considered, i.e., whether the visual object is part of (or contributes to) the current presentation content or not. Therefore, we propose a new approach that estimates the interest (or non-interest) of a user by means of dynamic Bayesian networks. Each of a predefined set of visual objects has a dynamic Bayesian network assigned to it, which calculates the current interest of the user in this object. The estimation takes into account (1) each new gaze point, (2) the current context of the object, and (3) preceding estimations of the object itself. Based on these estimations the presentation agents can provide timely and appropriate response.