EAGER: Learning Upsampling Operators for Animation of Cloth and Fluids

EAGER：学习布料和流体动画的上采样运算符

• 批准号: 1249756
• 负责人: Adam Bargteil
• 金额: $ 10万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1249756&HistoricalAwards=false
• 关键词: EAGER; Learning; Upsampling; Operators; Animation

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.

PI在这项探索性研究中的目标是解决阻碍自然现象的高质量交互式动画的根本障碍，即所涉及的大量自由度。 由于手工制作一个典型的网格动画是一项乏味而耗时的任务，计算机图形学已经转向物理和模拟来制作大多数自然现象的动画。 在这种情况下，模拟的承诺是通用性;可以探索材料属性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优势（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（高维度和计算费用）。 为此，他将开发机器学习工具，以寻找新的和更具表现力的低维表示，这些表示不描述所有可能的动画，而是简洁地描述所需动画的空间。 以前将机器学习应用于自然现象动画的尝试已经显示出希望，但也存在明显的局限性。 这些方法遭受过度拟合，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，这些方法过于数据驱动，未能考虑到宝贵的人类知识和直觉或数学和物理模型的输入。 除非这些限制得到解决，否则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。 具体来说，PI将专注于布和流体作为测试床域（最初假设通过数据驱动的上采样算子增强的粗糙模拟的算法范例），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：模拟是一种强大的技术，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计算机图形学领域，但项目成果将允许跨所有科学和工程的自然现象的高质量交互式计算机动画，特别适用于电影，视频游戏，虚拟现实，医疗培训等。计算机动画的独特环境将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计划在自由BSD许可证下开发和发布他的大部分源代码。