Reference number : GC-2017-Q2-00006, amendment number : , agreement type : G, recipient legal name : Popa, Tiberiu (Concordia University)|Popa, Tiberiu (Université Concordia), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Collaborative Research and Development Grants - Project, program name french : Subventions de recherche et développement coopérative - projet, program purpose english : Animation is a quintessential part of game playing experience since the dawn of computer gaming. With recent advances in animation acquisition and synthesis, computer animation in games is getting more and more complex and more and more realistic. This realism comes at a cost: the computational and memory requirements for new games are very high. The goal of this project is to propose a new type of animation controller for games that increases the realism of the animation while, at the same time, significantly reduces the computational and memory footprints. _x000D_
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We achieve this by using recent advances in artificial intelligence and neural networks. Current state of the art animation controllers store pre-recorded animations in memory, and at run-time they stitch them together to synthesize a new motion that conforms to the constraints that the character has such as terrain, collision with other objects, etc. In this work, instead of carrying all these pre-recorded animation in memory and using them to synthesize new motion, we will use them to learn the motion of a given character using state of the art deep learning techniques. Once the motion is learned, the synthesis is light-weight, and does not have a large memory footprint. Furthermore, the motion synthesis using neural networks is achieved by querying the neural network. This operation can be easily moved onto the cloud allowing the gaming console to release a lot of the processing time to other tasks. _x000D_
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The learning stage, on the other hand, is difficult and computationally expensive, but it needs to be done only once in an off-line manner and it can be done by the gaming company using a centralized cluster of computers. Using this new game controller concept effectively shifts the complexity of the process to an off-line process, leaving only light tasks to be performed in real-time. _x000D_
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We are collaborating closely on this project with UBISOFT, a leading company in computer games that has a large R&D studio in Montreal. Using this technique will achieve a significant paradigm shift in computer animation that has the potential of revolutionizing the computer gaming field thus benefiting the company, the computer animation community as well as the Canadian gaming industry as a whole. _x000D_
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_x000D_, program purpose french : Animation is a quintessential part of game playing experience since the dawn of computer gaming. With recent advances in animation acquisition and synthesis, computer animation in games is getting more and more complex and more and more realistic. This realism comes at a cost: the computational and memory requirements for new games are very high. The goal of this project is to propose a new type of animation controller for games that increases the realism of the animation while, at the same time, significantly reduces the computational and memory footprints. _x000D_
_x000D_
We achieve this by using recent advances in artificial intelligence and neural networks. Current state of the art animation controllers store pre-recorded animations in memory, and at run-time they stitch them together to synthesize a new motion that conforms to the constraints that the character has such as terrain, collision with other objects, etc. In this work, instead of carrying all these pre-recorded animation in memory and using them to synthesize new motion, we will use them to learn the motion of a given character using state of the art deep learning techniques. Once the motion is learned, the synthesis is light-weight, and does not have a large memory footprint. Furthermore, the motion synthesis using neural networks is achieved by querying the neural network. This operation can be easily moved onto the cloud allowing the gaming console to release a lot of the processing time to other tasks. _x000D_
_x000D_
The learning stage, on the other hand, is difficult and computationally expensive, but it needs to be done only once in an off-line manner and it can be done by the gaming company using a centralized cluster of computers. Using this new game controller concept effectively shifts the complexity of the process to an off-line process, leaving only light tasks to be performed in real-time. _x000D_
_x000D_
We are collaborating closely on this project with UBISOFT, a leading company in computer games that has a large R&D studio in Montreal. Using this technique will achieve a significant paradigm shift in computer animation that has the potential of revolutionizing the computer gaming field thus benefiting the company, the computer animation community as well as the Canadian gaming industry as a whole. _x000D_
_x000D_
_x000D_
_x000D_
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_x000D_, agreement title english : Next generation motion controller and synthesis for game characters, agreement title french : Next generation motion controller and synthesis for game characters, agreement number : CRDPJ, agreement value : 150000.0, agreement start date : 2017-07-12, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2020-2021)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2020-2021)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada