the effect of interface on social action in online virtual worlds anthony steed department of...
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The Effect of Interface on Social Action in Online Virtual Worlds
Anthony Steed
Department of Computer Science
University College London
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Avatar Puppet Systems
• From the very early systems common behaviours emerged– Customisation of representation– Spatial group-forming behaviours– Social reactions– “Presence” or not in your avatar (i.e. being at the
keyboard) needs to be signalled with gestures, otherwise difficult to interact
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Outline: Transparency & Boundaries
• Puppeteering systems take effort to express activity and motion
• Can be difficult for users to understand intentions and actions
• Immersive systems alleviate some of these barriers by making the interface transparent
• Do this by engaging the the user(s) in the virtual world, but we can envisage mixed-reality systems that break the boundary in a different way
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Immersive Interfaces
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Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
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Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
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Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
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Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
“I felt as if I was being watched”
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Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
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Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
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Why is Collaboration So Effective?
• Tracked gestures are immediately communicative• It is very easy to interpret gaze and pointing of the
other• Immersed users spend very little time
“manipulating” the interface
• Indeed in other experiments, users with immersive interfaces emerged as leaders over desktop interfaces users
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Forgetting Which Hand is Which
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Capturing the User
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Eyecatching: Eyetracking Generation 2
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Experiment Outline
• Task– Grab-Instructions
– Position-Instructions
• Measures– Time to do both types of instruction
– Errors in both types of instruction
– Conversational analysis
• Conditions– No eye movement
– Modelled eye gaze
– Tracked eye gaze
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Results
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Conversational Analysis
• Success– “OK, can you pick this cube”
– “This one?”
– “Yes”
Look at speaker
Look from head to cube
Point at cubeLook at speaker
Mutual gaze
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Look at speaker
Look at speaker
Look at speaker
Conversational Analysis
• Failure– “OK, can you pick this cube”
– “This one?”
– “No, this one”, “This one?”
Look at speaker
Look from head to wrong cube
Point at wrong cubeLook at speaker
Mutual gaze
Look at cube
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Technical Challenges
• Avatar representation• Lip synchronisation• End-to-end latency• Frame rate• Motion capture
• Capture real world so you can talk about it
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Eye Gaze
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Key Aspects
• Comparison with video as benchmark• Subjects answer as series of questions to a
confederate• Stage 1: Do users exhibit characteristic gaze,
blink and pupil dilation when they talk to a video or avatar-mediated representation of a questioner?
• Stage 2: Can independent observers detect lying when it is presented as an avatar?
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Stage 1: Key Findings
• Participants have similar behaviour when speaking to an avatar or a video
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Stage 2: Key Findings
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Breaking Boundaries
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32
CaptureDestination
CaptureVisitor
DisplayVisitor
DisplayDestination
\\\\\
Asymmetric Collaboration
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Immersion is great but …
• Immersive interfaces are expensive• You are bound to the metaphor where there is a
virtual place you go to
• Bring the avatar to you– Make it aware of the user and the space around you– Interpret the real world and interact with it
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34
Demo Highlights
Panoramic Camera(PointGrey Ladybug 3)
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Conclusions
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Conclusions
• Collaboration can be very fluid with immersive interfaces– Several challenges remain concerning capture– Desirable to bring more of those capabilities to non-
immersive (passive capture) systems
• Many rules that can be applied to agents• We expect that asymmetric collaboration
situations will be more common in the future and this deserves further attention
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Acknowledgements
• Eyecatching– William Steptoe, Robin Wolff, Alessio Murgia, Estefania
Guimaraes, John Rae, Wole Oyekoya
• Presenccia– Wole Oyekoya
• BEAMING– Will Steptoe, Wole Oyekoya, Tim Weyrich, Fabrizio
Pece, Jan Kautz– Partners at UB, Jean-Marie Normand, Mel Slater– www.beaming-eu.org