A garment acquisition pipeline for game asset retargetting

用于游戏资产重新定位的服装收购渠道

• 批准号: 561038-2020
• 负责人: Popa, Tiberiu
• 金额: $ 1.46万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=723330
• 关键词: garment; acquisition; pipeline; game; asset

Cloth animation is ubiquitous in video games and a quintessential part of game playing experience since the dawn of computer gaming. Realistic cloth animation can be achieved using sophisticated cloth simulation engines, but in a real-time game the computing budget for cloth simulation simply does not allow for a detailed cloth simulation. Therefore, practical solutions generally revolve around augmenting simplified simulation techniques: a simple simulation at the base augmented by keyframing or other animation techniques for increased animator control. This approach has a number of shortcomings from lack of animator control, to simulation stability as well as performance. In this work we propose a fundamentally different approach where we use off-line cloth acquisition to drive the cloth synthesis process in a homologue way to how motion capture can drive the character motion synthesis process. We use performance captures of cloth as assets to be retargeted in real-time based on a never seen before character and motion. We achieve this by using the recent advances in artificial intelligence and neural networks. We define the animation of a specific garment as a four-dimensional regression function of body shape, body dynamics and garment properties and we frame the problem as an optimization function where the input is body rest shape, body motion, garment rest-shape and the output.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.

布料动画在视频游戏中无处不在，也是自电脑游戏诞生以来游戏体验的精华部分。逼真的布料动画可以使用复杂的布料模拟引擎来实现，但在实时游戏中，布料模拟的计算预算根本不允许进行详细的布料模拟。因此，实用的解决方案通常围绕增强简化的模拟技术展开：通过关键帧或其他动画技术增强基础上的简单模拟，以增强动画师控制。这种方法有许多缺点，从缺乏动画师控制，到模拟的稳定性以及性能。在这项工作中，我们提出了一种完全不同的方法，其中我们使用离线布料采集来驱动布料合成过程，类似于运动捕捉如何驱动角色运动合成过程。我们使用布料的性能捕获作为资源，以基于前所未见的角色和运动实时重定目标。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点。我们将特定服装的动画定义为身体形状、身体动力学和服装属性的四维回归函数，并将问题框定为一个优化函数，其中输入是身体休息形状、身体运动、服装休息形状和输出。我们正在与电脑游戏行业的领先公司育碧密切合作，育碧在蒙特利尔拥有一个大型研发工作室。使用这一技术将实现计算机动画的重大范式转变，这将有可能给计算机游戏领域带来革命性的变化，从而使公司、计算机动漫界以及整个加拿大游戏行业受益。