BibTex
@inproceedings{Pagurek van Mossel:2019:10.20380/GI2019.12,
author = {Pagurek van Mossel, Dave and Madan, Abhishek and Liu, Tai Meng and Bardea, Paul and McBurney, Andrew},
title = {Controlling Procedural Modelling Interactively with Guiding Curves},
booktitle = {Proceedings of Graphics Interface 2019},
series = {GI 2019},
year = {2019},
issn = {0713-5424},
isbn = {978-0-9947868-4-5},
location = {Kingston, Ontario},
numpages = {8},
doi = {10.20380/GI2019.12},
publisher = {Canadian Information Processing Society},
}Supplementary Media
Abstract
Grammar-based procedural modelling on its own produces a larger space of generated models than is artistically desirable. Probabilistic sampling techniques can help search this result space for models that best fit a set of constraints. We aim to provide a useful probabilistic search function that can be run at interactive rates to enable the short feedback loops artists require for incremental, exploratory design. We present a constraint for use in Sequential Monte Carlo optimization where artists draw curves to guide the generation of models. The high-level structure of models can be intuitively specified by our constraint framework, allowing for variation in low-level details to be automatically filled in. We present a real-time model editor to demonstrate the artistic utility of our method.