Fast Neural Network Emulation of Dynamical Systems for Computer Animation
Abstract
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. We demonstrate NeuroAnimators for a va(cid:173) riety of physics-based models.
Cite
Text
Grzeszczuk et al. "Fast Neural Network Emulation of Dynamical Systems for Computer Animation." Neural Information Processing Systems, 1998.Markdown
[Grzeszczuk et al. "Fast Neural Network Emulation of Dynamical Systems for Computer Animation." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/grzeszczuk1998neurips-fast/)BibTeX
@inproceedings{grzeszczuk1998neurips-fast,
title = {{Fast Neural Network Emulation of Dynamical Systems for Computer Animation}},
author = {Grzeszczuk, Radek and Terzopoulos, Demetri and Hinton, Geoffrey E.},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {882-888},
url = {https://mlanthology.org/neurips/1998/grzeszczuk1998neurips-fast/}
}