windows 10 mobile and distributed graphics

Windows 10 Mobile and Distributed Graphics

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


Research Interest

ComputerGraphics

Communication

Distributed mobile graphics


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


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


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


Key-frame interpolation animation Key-frame models in face animation = visemes


Traditional reduction methods


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?


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


Saving cellular data


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


Generating on demand


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


Delaunay triangulation

Our approach


Lot generation



Variations of street layout



Final result

CONCLUSIONS


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


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

[email protected]

29

windows 10 mobile and distributed graphics

Technology