Subspace Clothing Simulation Using Adaptive Bases


We present a new approach to clothing simulation using low-dimensional linear subspaces with temporally adaptive bases. 

July 27, 2014



Fabian Hahn (Disney Research/ETH Joint PhD)

Bernhard Thomaszewski (Disney Research)

Stelian Coros (Disney Research)

Robert W. Sumner (Disney Research)

Forrester Cole (Pixar Animation Studios)

Mark Meyer (Pixar Animation Studios)

Tony DeRose (Pixar Animation Studios)

Markus Gross (Disney Research/ETH Zurich)

Subspace Clothing Simulation Using 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.

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