Poster
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
Daniel Ritchie · Anna Thomas · Pat Hanrahan · Noah Goodman

Mon Dec 5th 06:00 -- 09:30 PM @ Area 5+6+7+8 #161 #None

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models in computer graphics, but they require many samples to produce desirable results. In this paper, we show how to create procedural models which learn how to satisfy constraints. We augment procedural models with neural networks which control how the model makes random choices based on the output it has generated thus far. We call such models neurally-guided procedural models. As a pre-computation, we train these models to maximize the likelihood of example outputs generated via SMC. They are then used as efficient SMC importance samplers, generating high-quality results with very few samples. We evaluate our method on L-system-like models with image-based constraints. Given a desired quality threshold, neurally-guided models can generate satisfactory results up to 10x faster than unguided models.

Author Information

Daniel Ritchie (Stanford University)
Anna Thomas (Stanford University)
Pat Hanrahan (Stanford University)
Noah Goodman (Stanford University)

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