NeuroAnimator

Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey Hinton
1998 Proceedings of the 25th annual conference on Computer graphics and interactive techniques - SIGGRAPH '98  
Animation through the numerical simulation of physicsbased graphics models offers unsurpassed realism, but it can be computationally demanding. Likewise, the search for controllers that enable physics-based models to produce desired animations usually entails formidable computational cost. This paper demonstrates the possibility of replacing the numerical simulation and control of dynamic models with a dramatically more efficient alternative. In particular, we propose the NeuroAnimator, a novel
more » ... approach to creating physically realistic animation that exploits neural networks. NeuroAnimators are automatically trained off-line to emulate physical dynamics through the observation of physicsbased models in action. Depending on the model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conventional numerical simulation. Furthermore, by exploiting the network structure of the NeuroAnimator, we introduce a fast algorithm for learning controllers that enables either physics-based models or their neural network emulators to synthesize motions satisfying prescribed animation goals. We demonstrate NeuroAnimators for a variety of physics-based models.
doi:10.1145/280814.280816 dblp:conf/siggraph/GrzeszczukTH98 fatcat:kjjczayu5ja43e5a4gbz33v7aa