Computer animation through the numerical simulation of physics-based graphics models offers unsurpassed realism, but it can be computation(cid:173) 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(cid:173) roAnimators are automatically trained off-line to emulate physical dy(cid:173) namics through the observation of physics-based models in action. De(cid:173) pending on the model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conven(cid:173) tional numerical simulation.

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.

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.

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 areautomatically trained off-line to emulate physical dynamics through the observation of physics-based models in action. Depending onthe model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conventional numericalsimulation. We demonstrate NeuroAnimators for a variety of physics-based models. 1 Introduction Animation based on physical principles has been an influential trend in computer graphics for over a decade (see, e.g., [1, 2, 3]). This is not only due to the unsurpassed realism that physics-based techniques offer.