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The Effect of Interface on Social Action in Online Virtual Worlds
Anthony Steed
Department of Computer Science
University College London
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
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
Immersive Interfaces
Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
“I felt as if I was being watched”
Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
Effectiveness of Immersive Interaction
Subjective Report
Tasks
Cognitive & Emotional
Behaviour
PhysiologicalAutonomic
Social
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
Forgetting Which Hand is Which
Capturing the User
Eyecatching: Eyetracking Generation 2
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
Results
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
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
Technical Challenges
• Avatar representation• Lip synchronisation• End-to-end latency• Frame rate• Motion capture
• Capture real world so you can talk about it
Eye Gaze
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?
Stage 1: Key Findings
• Participants have similar behaviour when speaking to an avatar or a video
Stage 2: Key Findings
Breaking Boundaries
32
CaptureDestination
CaptureVisitor
DisplayVisitor
DisplayDestination
\\\\\
Asymmetric Collaboration
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
34
Demo Highlights
Panoramic Camera(PointGrey Ladybug 3)
Conclusions
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
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
