Towards Visual Foundational Models of Physical Scenes
Chethan Parameshwara Alessandro Achille Matthew Trager Xiaolong Li Jiawei Mo Matthew Trager Ashwin Swaminathan CJ Taylor Dheera Venkatraman Xiaohan Fei Stefano Soatto
We describe a first step towards learning general-purpose visual
representations of physical scenes using only image prediction as a training
criterion. To do so, we first define "physical scene" and show that, even
though different agents may maintain different representations of the same
scene, the underlying physical scene that can be inferred is unique. Then, we
show that NeRFs cannot represent the physical scene, as they lack extrapolation
mechanisms. Those, however, could be provided by Diffusion Models, at least in
theory. To test this hypothesis empirically, NeRFs can be combined with
Diffusion Models, a process we refer to as NeRF Diffusion, used as unsupervised
representations of the physical scene. Our analysis is limited to visual data,
without external grounding mechanisms that can be provided by independent
sensory modalities.