The document discusses distributed graphics techniques for mobile devices. It describes research into rendering facial models more efficiently through viseme reduction, collaborative device-to-device video streaming to save cellular data, and generating virtual cities on mobile devices using a stateless generation approach. The goals are to bring new graphical scenarios to mobile devices despite their limited resources and focus on advanced wireless communication architectures.

1. WINDOWS 10 MOBILE AND
DISTRIBUTED GRAPHICS
JIŘÍ DANIHELKA

2. 11.12.2015 11:00
 Motivation
 Rendering of Facial Models
 Collaborative Device-to-Device Video Streaming
 Virtual Cities on Mobile Devices
 Conclusion
OVERVIEW
Jiří Danihelka - Distributed Mobile Graphics2

3. MOTIVATION

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Research Interest
Jiří Danihelka - Distributed Mobile Graphics
Computer
Graphics
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)
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Mobile Graphics
Jiří Danihelka - Distributed Mobile Graphics

6. 11.12.2015 11:00
GOALS:
1. Bring previously not possible graphical scenarios
to mobile devices despite their limited resources
2. Focus on advanced wireless communication
architectures for mobile graphics
METHODOLOGY:
 Use validation using mathematical proof,
implementations and measurements
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Goals
Jiří Danihelka - Distributed Mobile Graphics

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 Mobile computing is a mass market
 Still limited resources (bandwidth, memory, …)
 Challenge for collaborative applications
Mobile Graphics - Characteristics
Jiří Danihelka - Distributed Mobile Graphics7
New technologies emerge
2005: Embedded systems 2010: Smartphones 2015: Wearables
Published 3 technical papers about wearables since
submission of my thesis

8. 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
Jiří Danihelka - Distributed Mobile Graphics

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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

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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

14. COLLABORATIVE DEVICE-TO-
DEVICE VIDEO STREAMING

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Saving cellular data
Jiří Danihelka - Distributed Mobile Graphics

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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)
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Video dissemination strategy
Jiří Danihelka - Distributed Mobile Graphics
playing buffered partially buffered not buffered

17. VIRTUAL CITIES ON MOBILE DEVICES

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Generating on demand
Jiří Danihelka - Distributed Mobile Graphics

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Generating on demand
Jiří Danihelka - Distributed Mobile Graphics

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Generating on demand
Jiří Danihelka - Distributed Mobile Graphics

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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
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Stateless generation
Jiří Danihelka - Distributed Mobile Graphics

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 Delaunay triangulation
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Our approach
Jiří Danihelka - Distributed Mobile Graphics

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 Lot generation
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Stateless generation
Jiří Danihelka - Distributed Mobile Graphics

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 Variations of street layout
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Stateless generation
Jiří Danihelka - Distributed Mobile Graphics

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Final result

26. 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
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Conclusions
Jiří Danihelka - Distributed Mobile Graphics

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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)
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Conclusions
Jiří Danihelka - Distributed Mobile Graphics

29. Thanks for your attention!
Jiří Danihelka
danihelka@live.com
Jiří Danihelka - Distributed Mobile Graphics29