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Foveated imaging provides a better tradeoff between situational awareness (field of view) and resolution, and is critical in long wavelength infrared regimes because of the size, weight, power, and cost of thermal sensors. We demonstrate computational foveated imaging by exploiting the ability of a meta-optical frontend to discriminate between different polarization states and a computational backend to reconstruct the captured image/video. The frontend is a three-element optic: the first element, which we call the “foveal” element, is a metalens that focuses s-polarized light at a distance off₁without affecting the p-polarized light; the second element, which we call the “perifovea” element, is another metalens that focuses p-polarized light at a distance off₂without affecting thes-polarized light. The third element is a freely rotating polarizer that dynamically changes the mixing ratios between the two polarization states. Both the foveal element (focal length=150mm; diameter=75mm) and the perifoveal element (focal length=25mm; diameter=25mm) were fabricated as polarization-sensitive, all-silicon, meta surfaces resulting in a large-aperture, 1:6 foveal expansion, thermal imaging capability. A computational backend then utilizes a deep image prior to separate the resultant multiplexed image or video into a foveated image consisting of a high resolution center and a lower-resolution large field of view context. We build a prototype system and demonstrate 12 frames per second real-time, thermal, foveated image and video capture..