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06-Dec-2019 10:16

All these data are available to app developers, which is one reason for the proliferation of apps to manipulate the face, such as Mug Life, which takes single photos and turns them into quasi-realistic fake videos on command.

All this work, which was incredibly difficult a decade ago, and possible only on cloud servers very recently, now runs right on the phone, as Apple has described.

But it’s not just that the camera knows there’s a face and where the eyes are.

Cameras also now capture multiple images in the moment to synthesize new ones.

The one the company has described publicly helps with white balancing—which helps deliver realistic color in a picture—in low light.

It also told the Verge that “its machine learning detects what objects are in the frame, and the camera is smart enough to know what color they are supposed to have.” Consider how different that is from a normal photograph.

Night Sight, a new feature for the Google Pixel, is the best-explained example of how this works.

Google developed new techniques for combining multiple inferior (noisy, dark) images into one superior (cleaner, brighter) image.

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The model was too big, though, so they trained a smaller version on the outputs of the first. Every photo every i Phone takes is thanks, in some small part, to these millions of images, filtered twice through an enormous machine-learning system.There are continuities with pre-existing techniques, of course, but only if you plot the progress of digital photography on some kind of logarithmic scale.High-dynamic range, or HDR, photography became popular in the 2000s, dominating the early photo-sharing site Flickr.Put them all together, and they could generate beautiful surreality.

In the right hands, an HDR photo could create a scene that is much more like what our eyes see than what most cameras normally produce.What makes the i Phone XS’s skin-smoothing remarkable is that it is simply the default for the camera. Now, under the hood, phone cameras pull information from multiple image inputs into one picture output, along with drawing on neural networks trained to understand the scenes they’re being pointed at.