Event-based cameras bring a unique capability to tracking, being able to function in
challenging real-world conditions as a direct result of their high temporal resolution and high …

In this paper we propose the Matting Anything Model (MAM) an efficient and versatile
framework for estimating the alpha matte of any instance in an image with flexible and …

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 …

We introduce a robust, real-time, high-resolution human video matting method that achieves
new state-of-the-art performance. Our method is much lighter than previous approaches and …

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 …

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 …

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 …

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 …

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 …

Generating accurate pseudo-labels under the supervision of image categories is a crucial
step in Weakly Supervised Semantic Segmentation (WSSS). In this work, we propose a Mat …