Spatial Reasoners for Continuous Variables in Any Domain
This is an incremental contribution that provides a tool to facilitate research in spatial reasoning with generative models for researchers in machine learning and AI.
The authors tackled the challenge of high-effort infrastructure for generative reasoning with denoising models over continuous variables by developing Spatial Reasoners, a software framework that provides easy-to-use interfaces for variable mapping, model paradigms, and inference strategies, and made it openly available.
We present Spatial Reasoners, a software framework to perform spatial reasoning over continuous variables with generative denoising models. Denoising generative models have become the de-facto standard for image generation, due to their effectiveness in sampling from complex, high-dimensional distributions. Recently, they have started being explored in the context of reasoning over multiple continuous variables. Providing infrastructure for generative reasoning with such models requires a high effort, due to a wide range of different denoising formulations, samplers, and inference strategies. Our presented framework aims to facilitate research in this area, providing easy-to-use interfaces to control variable mapping from arbitrary data domains, generative model paradigms, and inference strategies. Spatial Reasoners are openly available at https://spatialreasoners.github.io/