Abstract
Image compositing is a key step in film making and image editing that aims to segment a foreground object and combine it with a new background. Automatic image compositing can be done easily in a studio using chroma-keying when the background is pure blue or green. However, image compositing in natural scenes with complex backgrounds remains a tedious task, requiring experienced artists to hand-segment. In order to achieve automatic compositing in natural scenes, we propose a fully automated method that integrates instance segmentation and image matting processes to generate high-quality semantic mattes that can be used for image editing task. Our approach can be seen both as a refinement of existing instance segmentation algorithms and as a fully automated semantic image matting method. It extends automatic image compositing techniques such as chroma-keying to scenes with complex natural backgrounds without the need for any kind of user interaction. The output of our approach can be considered as both refined instance segmentations and alpha mattes with semantic meanings. We provide experimental results which show improved performance results as compared to existing approaches.
Abstract (translated)
图像合成是电影制作和图像编辑中的一个关键步骤，其目的是分割前景对象并将其与新的背景结合起来。当背景是纯蓝色或绿色时，使用色度键控可以很容易地在工作室中完成自动图像合成。然而，在背景复杂的自然场景中合成图像仍然是一项乏味的任务，需要有经验的艺术家手工分割。为了实现自然场景的自动合成，我们提出了一种将实例分割和图像铺垫过程相结合的全自动方法，以生成可用于图像编辑任务的高质量语义铺垫。我们的方法既可以看作是对现有实例分割算法的改进，也可以看作是一种完全自动化的语义图像铺垫方法。它将自动图像合成技术（如色度键控）扩展到具有复杂自然背景的场景，而无需任何用户交互。我们的方法的输出可以被视为细化的实例分段和具有语义意义的alpha mattes。我们提供的实验结果表明，与现有方法相比，性能得到了改善。
URL
https://arxiv.org/abs/1904.05457
