Paper
Fast Neural Network Emulation of Dynamical Systems for Computer Animation
Computer animation through the numerical simulation of physics-based graphics models offers unsurpassed realism, but it can be computation-ally demanding. This paper demonstrates the possibility of replacing the numerical simulation of nontrivial dynamic models with a dramatically more efficient "NeuroAnimator " that exploits neural networks. Neu-roAnimators are automatically trained off-line to emulate physical dy-namics through the observation of physics-based models in action. De-pending on the model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conven-tional numerical simulation. We demonstrate NeuroAnimators for a va-riety of physics-based models. 1
Authors: Radek Grzeszczuk · Demetri Terzopoulos · Geoffrey E. Hinton