论文标题
背景垫子：世界是您的绿屏
Background Matting: The World is Your Green Screen
论文作者
论文摘要
我们提出了一种通过手持式相机在日常设置中拍摄照片或视频来创建一个人的哑光的方法 - 人均前景颜色和alpha。大多数现有的均值方法都需要绿屏背景或手动创建的三张图来产生良好的哑光。出现了自动，无构架的方法，但质量不可比拟。在我们的Trimap免费方法中，我们要求用户在被捕获时拍摄背景的额外照片，而无需主题。此步骤需要少量的远见，但比创建三板架的时间要少得多。我们训练一个具有对抗性损失的深层网络，以预测哑光。我们首先训练一个具有合成复合材料的地面真相数据损失的垫子网络。为了在没有标签的情况下弥合域间隙，以弥补真实图像，我们训练另一个由第一个网络和判断复合材料质量的歧视者指导的垫子网络。我们在各种照片和视频上展示了结果，并在最新情况下显示出显着改善。
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.
