  We propose a method for creating a matte -- the per-pixel foreground color
and alpha -- of a person by taking photos or videos in an everyday setting with
a handheld camera. Most existing matting methods require a green screen
background or a manually created trimap to produce a good matte. Automatic,
trimap-free methods are appearing, but are not of comparable quality. In our
trimap free approach, we ask the user to take an additional photo of the
background without the subject at the time of capture. This step requires a
small amount of foresight but is far less time-consuming than creating a
trimap. We train a deep network with an adversarial loss to predict the matte.
We first train a matting network with supervised loss on ground truth data with
synthetic composites. To bridge the domain gap to real imagery with no
labeling, we train another matting network guided by the first network and by a
discriminator that judges the quality of composites. We demonstrate results on
a wide variety of photos and videos and show significant improvement over the
state of the art.
