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Influence of Animated Reality Mixing
Techniques on User Experience
Fabio Zünd1, Marcel Lancelle1, Mattia Ryffel2, Robert W.
Sumner1,2, Kenny Mitchell2, Markus Gross1,2
1ETH
Zurich, 2Disney Research Zurich
Switzerland
Motivation
• Augmented Reality relies on mixing techniques
Camera image
(BG)
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Virtual content
(FG)
Related Work
• Camera artifacts rendering [Klein and Murray 2010]
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Related Work
• Perception study: Motion blur was noticeable but had no
effect on performance in a racing game [Sharan et al. 2013]
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Overview
• Three experiments testing different aspects of mixing techniques
– Motion blur
– Latency
– Visual realism
• Record user’s experience and performance
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ARTravelers
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Experiment 1: Motion Blur
• Depends on velocity of objects and camera exposure
time
• Three different motion blur configurations
– A: no blur (bright scene)
– B: only BG blur (dark scene, not matching FG and BG)
– C: FG and BG blur (dark scene, matching FG and BG)
• Hypothesis: In B player performance and experience is worse
than in A an C.
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Experiment 1: Motion Blur
• 12 players, each 5 runs (1st run is training), 60 sec
• Capture
– User’s total score
– Questions (1 to 5):
• Enjoyment
• Score satisfaction
• Matching of FG and BG
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Results Experiment 1: Motion Blur
• Multivariate analysis shows no significant effect
Player Experience
Player Performance
Enjoyment
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Satisfaction
Matching
Results Experiment 1: Motion Blur
• Correlation matrix
confirms results
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Experiment 2: Latency
• Latency caused by hardware and software
• Add artificial video latency
– Between 0 ms and 1200 ms
• Hypothesis: Latency decreases performance and
experience
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Experiment 2: Latency
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Experiment 2: Latency
• 9 players, each 7 runs (1st, 2nd run is training), 40 sec
• Capture
– User’s total score
– Questions (1 to 5):
• Enjoyment
• Score satisfaction
• Responsiveness
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Results Experiment 2: Latency
• Linear regression model
• Latency has significant negative effect on score, satisfaction,
and responsiveness
Score
(p=0.009)
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Enjoyment
(p=0.064)
Score satisfaction
(p=0.002)
Responsiveness
(p=0.001)
Results Experiment 2: Latency
• Correlation matrix
confirms results
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ARPix
• Take a picture with the virtual character Eva
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Experiment 3: Lighting/Camera Artifacts
• Participant is presented
with 4 versions
• Chooses preferred one
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Experiment 3: Lighting/Camera Artifacts
• Correct lighting
• Camera artifacts on
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Experiment 3: Lighting/Camera Artifacts
• Correct lighting
• Camera artifacts off
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Experiment 3: Lighting/Camera Artifacts
• Incorrect lighting
• Camera artifacts on
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Experiment 3: Lighting/Camera Artifacts
• Incorrect lighting
• Camera artifacts off
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Results Experiment 3: Lighting/Camera Artifacts
•
•
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Chi-square goodness-of-fit test
confirms significant
differences in groups A, B, C, D
Majority choose version A
(correct lighting, artifacts on)
Location 1 (32 users)
Location 2 (40 users)
Chi-square(3)=17.545
p=0.001
Chi-square(3)=17.157
p=0.001
Conclusion
• In our Augmented Reality games
– Even strong motion blur has only little effect
– Latency strongly correlates with player performance and
experience
– Subtle visual realism is noticed and preferred
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Thank you!
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