Estimating human pose and shape from monocular images is a long-standing problem in
computer vision. Since the release of statistical body models, 3D human mesh recovery has …

Human parsing aims to partition humans in image or video into multiple pixel-level semantic
parts. In the last decade, it has gained significantly increased interest in the computer vision …

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 …

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 …

We show that existing upsampling operators can be unified using the notion of the index
function. This notion is inspired by an observation in the decoding process of deep image …

Compared to many other dense prediction tasks, eg, semantic segmentation, it is the
arbitrary number of instances that has made instance segmentation much more challenging …

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) …

Natural image matting algorithms aim to predict the transparency map (alpha-matte) with the
trimap guidance. However, the production of trimap often requires significant labor, which …

Extracting accurate foregrounds from natural images benefits many downstream
applications such as film production and augmented reality. However, the furry …