Image matting is an inverse fusion process that separates the foreground and background
information by predicting alpha matte for each pixel. Recently, plain vision Transformers …

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 …

Compositing a person into a scene to look like they are really there is a fundamental
technique in visual effects, with many other applications such as smartphone photography …

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 …

In this paper, we propose a transformer-based image matting model called MatteFormer,
which takes full advantage of trimap information in the transformer block. Our method first …

Existing deep learning based matting algorithms primarily resort to high-level semantic
features to improve the overall structure of alpha mattes. However, we argue that advanced …

Image matting refers to extracting precise alpha matte from natural images, and it plays a
critical role in various downstream applications, such as image editing. Despite being an ill …

Existing portrait matting methods either require auxiliary inputs that are costly to obtain or
involve multiple stages that are computationally expensive, making them less suitable for …

Natural image matting separates the foreground from background in fractional occupancy
which can be caused by highly transparent objects, complex foreground (eg, net or tree) …

Abstract We propose Mask Guided (MG) Matting, a robust matting framework that takes a
general coarse mask as guidance. MG Matting leverages a network (PRN) design which …