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We introduce Magenta Green Screen, a novel machine learning--enabled mattingtechnique for recording the color image of a foreground actor and asimultaneous high-quality alpha channel without requiring a special camera ormanual keying techniques. We record the actor on a green background but lightthem with only red and blue foreground lighting. In this configuration, thegreen channel shows the actor silhouetted against a bright, even background,which can be used directly as a holdout matte, the inverse of the actor's alphachannel. We then restore the green channel of the foreground using a machinelearning colorization technique. We train the colorization model with anexample sequence of the actor lit by white lighting, yielding convincing andtemporally stable colorization results. We further show that time-multiplexingthe lighting between Magenta Green Screen and Green Magenta Screen allows thetechnique to be practiced under what appears to be mostly normal lighting. Wedemonstrate that our technique yields high-quality compositing results whenimplemented on a modern LED virtual production stage. The alpha channel dataobtainable with our technique can provide significantly higher quality trainingdata for natural image matting algorithms to support future ML mattingresearch.

Magenta Green Screen: Spectrally Multiplexed Alpha Matting with Deep Colorization