NeuroAnimator: fast neural network emulation and control of physics-based models

Abstract

Animation through the numerical simulation of physics- based 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 demon- strates the possibility of replacing the numerical simulation and control of dynamic models with a dramatically more efficient al- ternative. In particular, we propose the NeuroAnimator, a novel ap- proach to creating physically realistic animation that exploits neu- ral 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 net- work 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 NeuroAni- mator, we introduce a fast algorithm for learning controllers that en- ables 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 mod- els.