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arXiv · 2502.20371

Constrained Generative Modeling with Manually Bridged Diffusion Models

Saeid Naderiparizi·Xiaoxuan Liang·Berend Zwartsenberg·Frank Wood
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Abstract

In this paper we describe a novel framework for diffusion-based generative modeling on constrained spaces. In particular, we introduce manual bridges, a framework that expands the kinds of constraints that can be practically used to form so-called diffusion bridges. We develop a mechanism for combining multiple such constraints so that the resulting multiply-constrained model remains a manual bridge that respects all constraints. We also develop a mechanism for training a diffusion model that respects such multiple constraints while also adapting it to match a data distribution. We develop and extend theory demonstrating the mathematical validity of our mechanisms. Additionally, we demonstrate our mechanism in constrained generative modeling tasks, highlighting a particular high-value application in modeling trajectory initializations for path planning and control in autonomous vehicles.

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Saeid Naderiparizi, Xiaoxuan Liang, Berend Zwartsenberg, Frank Wood. 2025-02-27. Constrained Generative Modeling with Manually Bridged Diffusion Models. https://arxiv.org/abs/2502.20371

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