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 …

Estimating the pose of a human in 3D given an image or a video has recently received
significant attention from the scientific community. The main reasons for this trend are the …

Despite significant progress, we show that state of the art 3D human pose and shape
estimation methods remain sensitive to partial occlusion and can produce dramatically …

Inferring human-scene contact (HSC) is the first step toward understanding how humans
interact with their surroundings. While detecting 2D human-object interaction (HOI) and …

Human motion is fundamental to understanding behavior. Despite progress on single-image
3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce …

To understand how people look, interact, or perform tasks, we need to quickly and
accurately capture their 3D body, face, and hands together from an RGB image. Most …

Due to the lack of camera parameter information for in-the-wild images, existing 3D human
pose and shape (HPS) estimation methods make several simplifying assumptions: weak …

From an image of a person in action, we can easily guess the 3D motion of the person in the
immediate past and future. This is because we have a mental model of 3D human dynamics …

Modern computer vision algorithms typically require expensive data acquisition and
accurate manual labeling. In this work, we instead leverage the recent progress in computer …

Recovering high-quality 3D human motion in complex scenes from monocular videos is
important for many applications, ranging from AR/VR to robotics. However, capturing …