Next generation motion controller and synthesis for game characters

下一代运动控制器和游戏角色合成

• 批准号: 505237-2016
• 负责人: Popa, Tiberiu
• 金额: $ 3.28万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689050
• 关键词: Next; generation; motion; controller; synthesis

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. 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. 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. 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.

动画是一个典型的一部分，游戏体验，因为黎明的电脑游戏。随着动画获取和合成技术的发展，游戏中的计算机动画变得越来越复杂，越来越逼真。这种真实感是有代价的：新游戏的计算和内存要求非常高。该项目的目标是提出一种新型的游戏动画控制器，增加动画的真实感，同时显着减少计算和内存占用。我们通过使用人工智能和神经网络的最新进展来实现这一目标。当前最先进的动画控制器将预先录制的动画存储在内存中，并在运行时将它们缝合在一起以合成符合角色所具有的约束（例如地形、与其他对象的碰撞等）的新运动。在这项工作中，不是将所有这些预先录制的动画存储在内存中并使用它们来合成新运动，我们将使用最先进的深度学习技术来学习给定角色的动作。一旦运动被学习，合成是轻量级的，并且不具有大的内存占用。此外，通过查询神经网络实现了基于神经网络的运动合成。这个操作可以很容易地移动到云上，允许游戏控制台释放大量的处理时间给其他任务。另一方面，学习阶段是困难的并且计算上昂贵的，但是它只需要以离线方式完成一次，并且它可以由游戏公司使用集中式计算机集群来完成。使用这种新的游戏控制器概念有效地将复杂的过程转移到离线过程，只留下实时执行的轻任务。我们正在与UBISOFT密切合作，UBISOFT是一家领先的电脑游戏公司，在蒙特利尔设有大型研发工作室。使用这种技术将实现计算机动画的重大范式转变，有可能彻底改变计算机游戏领域，从而使公司，计算机动画社区以及整个加拿大游戏行业受益。