#BLUE AND RED BACKGROUND TV#
the framerate most films and TV are shot), but if they switch the light up faster - 144 times per second - it appears “nearly constant.”
This is distracting (even dangerous) to do 24 times per second (i.e. The paper addresses this, however, with the possibility of “time-multiplexing” the lighting, essentially switching the magenta/green lighting on and off multiple times per second. Many actors already complain of how unnatural it is to work in front of a green screen - imagine doing it while lit in a harsh, inhuman light. So the color can be restored surprisingly well in post (it’s “virtually indistinguishable” from an in-camera ground truth) - but there’s still the problem of the actors and set having to be lit in this horrible way. The convolutional neural network is given patches of the full-spectrum image to compare to the magenta-lit ones, and develops a process for quickly restoring the missing green channel in a more intelligent manner than a simple algorithm.Ī simple algorithm leads to poor results (top) while a more sophisticated ML model produces colors very similar to ground truth.
The team trained a machine learning model on training data of their own, essentially “rehearsal” takes of similar scenes, but lit normally. How can it be automated? AI to the rescue! It must be done systematically and adaptively, since subjects and compositions differ, but a “naïve” linear approach to injecting green results in a washed out, yellowish look. Of course they seem to have just substituted one difficulty for another: The process of compositing is now easy, but restoring the green channel to the magenta-lit subjects is hard. This makes the resulting mattes extremely accurate, lacking the artifacts that come from having to separate a full-spectrum input from a limited-spectrum key background. A regular camera that would normally capture those colors instead captures red, blue and alpha. Behind them, bright green (actively lit, not a backdrop) in front, a mix of red and blue, making for a dramatically contrasting colors.īut the technique is also clever in that by making the foreground only red/blue and the background only green, it simplifies the process of separating the two. Netflix researchers are taking a shot at it, though, with a combination of old and new that could make for simple, immaculate compositing - at the cost of a hellish on-set lighting setup.Īs described in a recently published paper, their “Magenta Green Screen” produces impressive results by, essentially, putting the actors in a lighting sandwich. It’s usually good enough, though, that attempts to replace it with more sophisticated and expensive methods ( like a light field camera) have languished. It’s easy and cheap, but there are a few downsides to this, among them problems with transparent objects, fine details like hair and, of course, anything else with a similar color to the background. The foreground is said to be “matted” and the background is a transparent “alpha” channel manipulated along with the red, green and blue channels.
Netflix has a new technique that relies on machine learning to do some of the hard work, but it requires lighting actors in a garish magenta.įor decades the simplest method of compositing was chroma keying, in which actors stand against a brightly colored background (originally blue, later green) that can easily be identified and replaced with anything from a weather map to a battle with Thanos. The process of compositing, or placing actors in front of a background that’s not actually there, is as old as filmmaking itself - and it’s always been a pain.
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