If you have some numbers please share. I've seen NVIDIA and Tobii (highly motivated parties) claim that you might get a 10% power reduction, but those studies

If you have some numbers please share. I've seen NVIDIA and Tobii (highly motivated parties) claim that you might get a 10% power reduction, but those studies were based on very constrained operating conditions and conveniently brush certain details under the rug. All told, it's a lot of work and a lot complexity to chase something that will at most provide modest gains. The cameras on headsets face the world. So you need 2-4 additional cameras trained on the eyes. These camera each require power just to get images. For a decent UX you need tracking at ~120hz (60hz eye tracking is slow). Exact figures are hard to come by on a Sunday morning, but off the top of my head, I've seen figures to the tune of 250mW per camera (which ignores ISP and the compute to move the data around). Computer vision algorithms running at 120 hz are typically not cheap. Pupil Labs cites 50% CPU (on an i5) per eye[1]. Tobii doesn't publish their power consumption but it is also pretty steep. I'm sure there are optimizations here but all the specs I've seen blush at the power consumption required by a general purpose eye tracker. If you have figures that suggest otherwise I would be happy to see but every quantitative analysis I've seen comes up with a muddled response. If you already commit to running eye tracking for other reasons then it makes sense to run this rendering optimization, but I'm not sure if it really pays for its self. [1] https://pupil-labs.com/products/vr-ar/tech-specs/ https://pupil-labs.com/products/vr-ar/tech-specs/