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Grant: Discovery Grants Program - Individual

Concordia University

$15,000.00

Augmenting 3D Modeling and Animation Techniques Using Large Sensed Data

FY 2013-14

Summary
Three dimensional (3D) computer graphics has been and is my dominant area of research. In spite of manyadvances, creating complex 3D geometric models and animations continues to be effort intensive and poses many challenges. Today, the wide use of low cost cameras and other sensing devices enable rapid capture of image, video, 3D surface and motion data. Combined with continued growth in multi-core CPUs and many core GPUs, these technologies present tremendous opportunities to transform the way in which we can carry out 3D shape modeling and animation tasks. The specific research focus of this proposal is the investigation of computational methods that efficiently process the large volumes of data captured by such devices and derive suitable characterizing representations. These can then be used to augment techniques for 3D modeling and animation. Through this research I hope to answer some of the following questions: 1) What shape characteristics can be computed from 3D scans for use in shape reconstruction, retrieval, etc.? 2) Identify what are appropriate motion data characteristizations which help create/enhance animations? 3) How can we learn from image/video data to inform 3D shape and animation creation processes? 4) How can we expand 3D interaction with full body movements using sensed data from a Kinect like device? 5) What specific programming models and techniques do we need to parallelize above developed modelling and animation techniques on commodity heterogeneous computing environments? My research methodology is essentially driven by the cycle of research, analysis, new ideas, implement, test and verify. My plan is to build upon on-going research in my 3D Graphics lab which has the necessary infrastructure for graphics data capture. I will continue the existing collaboration with Fine Arts researchers to provide us with real-life usage scenarios in games and entertainment. The principal goal of this research is to make it easier for designers and animators to produce 3D models and animations for novel situations without requiring excessive user input, programming or human effort.
Program
Discovery Grants Program - Individual
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
261435-2013
Name nom
Mudur, SudhirPandurang
Department département
Computer Science and Software Engineering
Organization id
70
Institution établissement
Concordia University
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2013
Competition year année de concours
2013
Award amount
15000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
Research subject code
2707
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Graphics
Research subject fr
Infographie
Partie
1
Nb partie
5
Application title
Augmenting 3D Modeling and Animation Techniques Using Large Sensed Data
Keyword
NSERC
Application summary
Three dimensional (3D) computer graphics has been and is my dominant area of research. In spite of manyadvances, creating complex 3D geometric models and animations continues to be effort intensive and poses many challenges. Today, the wide use of low cost cameras and other sensing devices enable rapid capture of image, video, 3D surface and motion data. Combined with continued growth in multi-core CPUs and many core GPUs, these technologies present tremendous opportunities to transform the way in which we can carry out 3D shape modeling and animation tasks. The specific research focus of this proposal is the investigation of computational methods that efficiently process the large volumes of data captured by such devices and derive suitable characterizing representations. These can then be used to augment techniques for 3D modeling and animation. Through this research I hope to answer some of the following questions: 1) What shape characteristics can be computed from 3D scans for use in shape reconstruction, retrieval, etc.? 2) Identify what are appropriate motion data characteristizations which help create/enhance animations? 3) How can we learn from image/video data to inform 3D shape and animation creation processes? 4) How can we expand 3D interaction with full body movements using sensed data from a Kinect like device? 5) What specific programming models and techniques do we need to parallelize above developed modelling and animation techniques on commodity heterogeneous computing environments? My research methodology is essentially driven by the cycle of research, analysis, new ideas, implement, test and verify. My plan is to build upon on-going research in my 3D Graphics lab which has the necessary infrastructure for graphics data capture. I will continue the existing collaboration with Fine Arts researchers to provide us with real-life usage scenarios in games and entertainment. The principal goal of this research is to make it easier for designers and animators to produce 3D models and animations for novel situations without requiring excessive user input, programming or human effort.