Subspace clothing simulation using adaptive bases
ACM Trans. Graph.(2014)
摘要
We present a new approach to clothing simulation using low-dimensional linear subspaces with temporally adaptive bases. Our method exploits full-space simulation training data in order to construct a pool of low-dimensional bases distributed across pose space. For this purpose, we interpret the simulation data as offsets from a kinematic deformation model that captures the global shape of clothing due to body pose. During subspace simulation, we select low-dimensional sets of basis vectors according to the current pose of the character and the state of its clothing. Thanks to this adaptive basis selection scheme, our method is able to reproduce diverse and detailed folding patterns with only a few basis vectors. Our experiments demonstrate the feasibility of subspace clothing simulation and indicate its potential in terms of quality and computational efficiency.
更多查看译文
关键词
animation,physically-based simulation,subspace simulation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络