  We design a real-time portrait matting pipeline for everyday use,
particularly for "virtual backgrounds" in video conferences. Existing
segmentation and matting methods prioritize accuracy and quality over
throughput and efficiency, and our pipeline enables trading off a controllable
amount of accuracy for better throughput by leveraging online distillation on
the input video stream. We construct our own dataset of simulated video calls
in various scenarios, and show that our approach delivers a 5x speedup over a
saliency detection based pipeline in a non-GPU accelerated setting while
delivering higher quality results. We demonstrate that an online distillation
approach can feasibly work as part of a general, consumer level product as a
"virtual background" tool. Our public implementation is at
https://github.com/josephch405/jit-masker.
