A garment acquisition pipeline for game asset retargetting

自电脑游戏诞生以来，布料动画在电子游戏中无处不在，是游戏体验的重要组成部分。使用复杂的布料模拟引擎可以实现逼真的布料动画，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，实际的解决方案通常围绕着增强简化模拟技术：通过关键帧或其他动画技术增强基础的简单模拟，以增加动画师的控制。这种方法有许多缺点，从缺乏动画师控制，到模拟稳定性和性能。

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.