EAGER: Learning Upsampling Operators for Animation of Cloth and Fluids

PI在这项探索性研究中的目标是解决阻碍高质量的自然现象交互式动画的根本障碍，即涉及的大量自由度。因为手动动画一个典型的网格是一个繁琐和耗时的任务，计算机图形学已经转向物理和模拟动画大多数自然现象。在这种情况下，模拟的前景是通用性；可以探索材料性质和初始条件的无限空间。这种普遍性也是模拟的最大限制；可能的动画空间是巨大的，而理想的动画空间要小得多。PI的方法是利用模拟的强度（在各种条件下创建丰富动画数据的能力）来克服其最大的局限性（高维和计算费用）。为此，他将开发机器学习工具来寻找新的和更具表现力的低维表示，这些表示不是描述所有可能的动画，而是简洁地描述理想动画的空间。之前将机器学习应用于自然现象动画的尝试显示出了希望，但也有很大的局限性。这些方法存在过度拟合的问题，牺牲了局部性，并且不允许对可能的动画空间进行艺术控制。此外，这些方法过于依赖数据，没有考虑到宝贵的人类知识和直觉，也没有考虑到数学和物理模型。在解决这些限制之前，高质量的自然现象交互式计算机动画的前景将仍然遥不可及。具体而言，PI将专注于布料和流体作为测试平台域（最初假设由数据驱动的上采样算子增强的粗糙模拟算法范例），为此他将探索稀疏性，扩展特征集，组合算子和艺术控制的问题。更广泛的影响：模拟是一项强大的技术，其用途不仅限于计算机动画。因此，虽然测试平台领域属于传统的计算机图形学领域，但项目成果将允许在所有科学和工程领域对自然现象进行高质量的交互式计算机动画，特别适用于电影、视频游戏、虚拟现实、医疗培训等。此外，计算机动画的独特背景必然需要新的机器学习算法，这些算法也将反馈到该社区。PI计划在免费的BSD许可证下开发和发布他的大部分源代码。

The PI's goal in this exploratory research is to tackle the fundamental obstacle preventing high quality, interactive animation of natural phenomena, namely the enormous number of degrees of freedom involved. Because hand animating a typical mesh is a tedious and time consuming task, computer graphics has turned to physics and simulation to animate most natural phenomena. In this context, the promise of simulation is generality; an infinite space of material properties and initial conditions can be explored. This generality is also simulation's greatest limitation; the space of possible animations is vast, while the space of desirable animations is a great deal smaller. The PI's approach is to use simulation's strength (its ability to create rich animation data under a variety of conditions) to combat its greatest limitations (high dimensionality and computational expense). To this end, he will develop machine learning tools for finding new and more expressive low-dimensional representations, which do not describe all possible animations but rather succinctly describe the space of desirable animations. Previous attempts to apply machine learning to the animation of natural phenomena have shown promise, but also significant limitations. These approaches have suffered from over-fitting, have sacrificed locality, and have not allowed artistic control over the space of possible animations. Furthermore, these approaches have been too data-driven, failing to allow for the input of valuable human knowledge and intuition or mathematical and physical models. Until these limitations are addressed, the promise of high-quality interactive computer animation of natural phenomena will remain out of reach. For concreteness the PI will focus on cloth and fluids as test bed domains (initially assuming an algorithmic paradigm of coarse simulation enhanced by data-driven upsampling operators), for which he will explore questions of sparseness, expanded feature sets, combining operators, and artistic control. Broader Impacts: Simulation is a powerful technique whose usefulness is not limited to computer animation. So while the test bed domains fall within the realm of traditional computer graphics, project outcomes will allow for high-quality, interactive computer animation of natural phenomena across all of science and engineering, with particular applicability to film, video games, virtual reality, medical training, etc. Moreover, the unique context of computer animation will necessarily require new machine learning algorithms that will feed back into that community as well. The PI plans to develop and release the majority of his source code under free BSD licenses.