Abstract We present Vid2Avatar, a method to learn human avatars from monocular in-the-
wild videos. Reconstructing humans that move naturally from monocular in-the-wild videos …

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 present a method that generates expressive talking-head videos from a single facial
image with audio as the only input. In contrast to previous attempts to learn direct mappings …

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 …

Real-time rendering of photorealistic and controllable human avatars stands as a
cornerstone in Computer Vision and Graphics. While recent advances in neural implicit …

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 …

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 …

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 …