Instance Matting

Instance matting aims to precisely extract individual objects, particularly humans, from images and videos by generating an alpha matte for each instance. Recent research focuses on developing end-to-end models, often incorporating transformer architectures and techniques like sparse convolution and spatial attention, to efficiently and accurately produce multiple instance mattes simultaneously. This work is driven by the need for improved accuracy and speed, leading to the creation of new large-scale datasets and evaluation metrics tailored to this challenging task. The advancements in instance matting have significant implications for various applications, including video editing, augmented reality, and computer vision.