Physics-Informed Generative Networks

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Generative models, such as GANs and diffusion models, have yet to fully incorporate the nuances of physical information. In this presentation, we address this oversight from two distinct yet opposite angles. First, we’ll showcase how incorporating physical models into GANs can enhance their control and output quality. Conversely, we’ll explore how advanced large scale diffusion models, especially Stable Diffusion, can be harnessed to discern physical attributes from natural images.

Dr Fabio Pizzati is a postdoctoral researcher in the Torr Vision Group at the University of Oxford, working with Prof. Phil Torr. He is currently working on large scale diffusion models for image generation, and continual learning.