Title: Contrastive Diffusion Guidance for Spatial Inverse Problems
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Diffusion Models, Inverse Problems, Contrastive Learning, Spatial Inference

Abstract: We consider the inverse problem of reconstructing the spatial layout of a place, a home floorplan for example, from a user’s movements inside that layout. 
Direct inversion is ill-posed since many floorplans can explain the same movement trajectories. 
We adopt a diffusion-based posterior sampler to generate layouts consistent with the measurements.
While active research is in progress on generative inverse solvers, we find that the forward operator in our problem poses new challenges.
The path planning process inside a floorplan is a non-invertible, non-differentiable function, and causes instability while optimizing using the likelihood score. 
We break-away from existing approaches and reformulate the likelihood score in a smoother embedding space.
The embedding space is trained with a contrastive loss which brings compatible floorplans and trajectories close to each other, while pushing mismatched pairs far apart. 
We show that a surrogate form of the likelihood score in this embedding space is a valid approximation of the true likelihood score, making it possible to steer the denoising process towards the posterior.
Across extensive experiments, our model CoGuide produces more consistent floorplans from trajectories, and is more robust than differentiable-planner baselines and guided-diffusion methods.