distributed mobile graphics for windows 10 mobile
TRANSCRIPT
Distributed Mobile Graphics for Windows Phone
JIŘÍ DANIHELKA
11.12.2015 11:00 2 Jiří Danihelka - Distributed Mobile Graphics
Motivation
Rendering of Facial Models
Collaborative Device-to-Device Video Streaming
Virtual Cities on Mobile Devices
Conclusion
OVERVIEW
MOTIVATION
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Research Interest
ComputerGraphics
Communication
Distributed mobile graphics
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limited resources available (memory, CPU, battery)
unstable and paid wireless network connection
sandbox environment for applications – limited access to hardware
different usage scenarios – in use while on the move
additional sensors (accelerometer, GPS, camera)
Mobile Graphics
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GOALS:1. Bring previously not possible graphical scenarios
to mobile devices despite their limited resources2. Focus on advanced wireless communication
architectures for mobile graphicsMETHODOLOGY: Use validation using mathematical proof,
implementations and measurements
Goals
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Mobile computing is a mass market Still limited resources (bandwidth, memory, …) Challenge for collaborative applications
Mobile Graphics - Characteristics
New technologies emerge
2005: Embedded systems 2010: Smartphones 2015: Wearables
Published 3 technical papers about wearables since submission of my thesis
RENDERING OF FACIAL MODELS
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Key-frame interpolation animation Key-frame models in face animation = visemes
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Traditional reduction methods
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Viseme reduction
How does it work– find similar visemes
(e.g. “f” and “th”)– merge them together
Problems– How to find similar visemes?– How to merge them optimally?
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How to find similar visemes? Define a metric for visemes
– distance between two models A and B
vA,1
vA,2
vA,3
vB,1
vB,2
vB,3
11.12.2015 11:00 (13) Jiří Danihelka - Distributed Mobile Graphics
Semantic Reduction of Face Models
Proof that outputs of both our algoritms – viseme merging algorithm– viseme reduction (clustering) algorithm
are optimal for selected metrics
Reduced resources– saved 38% memory– 2.25 times faster startup
COLLABORATIVE DEVICE-TO-DEVICE VIDEO STREAMING
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Saving cellular data
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collaborative downloading can save– up to 40% data for video streaming, 25% on average
(real-life experiment at ETH Zurich)– even more for less time-sensitive data (updates, RSS feeds)
Video dissemination strategy
playing buffered partially buffered not buffered
VIRTUAL CITIES ON MOBILE DEVICES
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Generating on demand
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Generating on demand
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Generating on demand
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Traditionally– generators have to respect previous results– geometry generated so far– state of the other generators
Our approach– generators share only the initial seed– they do not have to synchronize their states– that is why we call the method Stateless generation– delivers consistent results regardless of the starting position– generated cities have no size limits – pseudo-infinite
Stateless generation
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Delaunay triangulation
Our approach
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Lot generation
Stateless generation
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Variations of street layout
Stateless generation
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Final result
CONCLUSIONS
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Bring previously not possible graphical scenarios to mobile devices despite their limited resources– Viseme-reduction reduces
memory requirements
– Stateless-generation generates only visible buildings
– Stateless-generation creates buildings locally on clients and greatly reduces data download
Conclusions
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Focus on advanced wireless communication architectures for mobile graphics– Discuss and compare architecture
for 3D head applications
– Proposed distributed collaborative video streaming
– Architecture used in stateless-generation method allows synchronization of generated cities (even when not always connected)
Conclusions
Jiří Danihelka - Distributed Mobile Graphics
Thanks for your attention!Jiří Danihelka
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