Name
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.20380/GI2019.12 | Proceedings of the 45th Graphics Interface Conference on Proceedings of Graphics Interface 2019 |
Keywords | Field | DocType |
Procedural modelling, interactive design | Computer science,Human–computer interaction | Conference |
ISBN | Citations | PageRank |
978-0-9947868-4-5 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dave Pagurek van Mossel | 1 | 0 | 0.34 |
| Abhishek Madan | 2 | 0 | 0.68 |
| Tai Meng Lui | 3 | 0 | 0.34 |
| Paul Bardea | 4 | 0 | 0.34 |
| Andrew McBurney | 5 | 0 | 0.34 |