  We introduce a real-time, high-resolution background replacement technique
which operates at 30fps in 4K resolution, and 60fps for HD on a modern GPU. Our
technique is based on background matting, where an additional frame of the
background is captured and used in recovering the alpha matte and the
foreground layer. The main challenge is to compute a high-quality alpha matte,
preserving strand-level hair details, while processing high-resolution images
in real-time. To achieve this goal, we employ two neural networks; a base
network computes a low-resolution result which is refined by a second network
operating at high-resolution on selective patches. We introduce two largescale
video and image matting datasets: VideoMatte240K and PhotoMatte13K/85. Our
approach yields higher quality results compared to the previous
state-of-the-art in background matting, while simultaneously yielding a
dramatic boost in both speed and resolution.
