Distributed Mobile Graphics for Windows 10 Mobile
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The document discusses distributed mobile graphics techniques for Windows Phone. It describes research into rendering facial models more efficiently using viseme reduction, collaborative device-to-device video streaming to save cellular data, and stateless generation of virtual cities on mobile devices to reduce data downloads. The goals are to bring advanced graphical scenarios to mobile devices despite their limited resources and to focus on wireless communication architectures for distributed mobile graphics.
Distributed Mobile Graphics for Windows 10 Mobile
- 1. DISTRIBUTED MOBILE GRAPHICS FOR WINDOWS PHONE 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
- 4. 11.12.2015 11:00 4 Research Interest Jiří Danihelka - Distributed Mobile Graphics Computer Graphics Communication Distributed mobile graphics
- 5. 11.12.2015 11:00 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) 5 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 6 Goals Jiří Danihelka - Distributed Mobile Graphics
- 7. 11.12.2015 11:00 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
- 9. 11.12.2015 11:00 9 Jiří Danihelka - Distributed Mobile Graphics Key-frame interpolation animation Key-frame models in face animation = visemes
- 10. 11.12.2015 11:00 10 Traditional reduction methods Jiří Danihelka - Distributed Mobile Graphics
- 11. 11.12.2015 11:00 11 Jiří Danihelka - Distributed Mobile Graphics 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?
- 12. 11.12.2015 11:00 12 Jiří Danihelka - Distributed Mobile Graphics 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
- 13. 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
- 14. COLLABORATIVE DEVICE-TO- DEVICE VIDEO STREAMING
- 15. 11.12.2015 11:00 15 Saving cellular data Jiří Danihelka - Distributed Mobile Graphics
- 16. 11.12.2015 11:00 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) 16 Video dissemination strategy Jiří Danihelka - Distributed Mobile Graphics playing buffered partially buffered not buffered
- 17. VIRTUAL CITIES ON MOBILE DEVICES
- 18. 11.12.2015 11:00 18 Generating on demand Jiří Danihelka - Distributed Mobile Graphics
- 19. 11.12.2015 11:00 19 Generating on demand Jiří Danihelka - Distributed Mobile Graphics
- 20. 11.12.2015 11:00 20 Generating on demand Jiří Danihelka - Distributed Mobile Graphics
- 21. 11.12.2015 11:00 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 21 Stateless generation Jiří Danihelka - Distributed Mobile Graphics
- 22. 11.12.2015 11:00 Delaunay triangulation 22 Our approach Jiří Danihelka - Distributed Mobile Graphics
- 23. 11.12.2015 11:00 Lot generation 23 Stateless generation Jiří Danihelka - Distributed Mobile Graphics
- 24. 11.12.2015 11:00 Variations of street layout 24 Stateless generation Jiří Danihelka - Distributed Mobile Graphics
- 25. 11.12.2015 11:00 25 Jiří Danihelka - Distributed Mobile Graphics Final result
- 26. CONCLUSIONS
- 27. 11.12.2015 11:00 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 27 Conclusions Jiří Danihelka - Distributed Mobile Graphics
- 28. 11.12.2015 11:00 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) 28 Conclusions Jiří Danihelka - Distributed Mobile Graphics
- 29. Thanks for your attention! Jiří Danihelka [email protected] Jiří Danihelka - Distributed Mobile Graphics29