Fast Neural Network Emulation and Control of Dynamical Systems

Computer animation through the numerical simulation of physics-based graphics models offers unsurpassed realism, but it can be computationally 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. NeuroAnimators are automatically trained off-line to emulate physical dynamics through the observation of physics-based 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. We demonstrate NeuroAnimators for a variety of physics-based models. By exploiting the network structure of the NeuroAnimator, we also introduce a remarkably fast algorithm for learning controllers that enables either complex physics-based models or their neural network emulators to synthesize motions satisfying prescribed animation goals.