Abstract Three-dimensional (3D) human pose estimation involves estimating the articulated
3D joint locations of a human body from an image or video. Due to its widespread …

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 …

We present an approach to reconstruct humans and track them over time. At the core of our
approach, we propose a fully" transformerized" version of a network for human mesh …

We tackle the problem of action-conditioned generation of realistic and diverse human
motion sequences. In contrast to methods that complete, or extend, motion sequences, this …

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 …

We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose
and shape. Though substantial progress has been made in estimating 3D human motion …

With the progress of 3D human pose and shape estimation, state-of-the-art methods can
either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases …

We present PyMAF-X, a regression-based approach to recovering a parametric full-body
model from a single image. This task is very challenging since minor parametric deviation …

We present a unified perspective on tackling various human-centric video tasks by learning
human motion representations from large-scale and heterogeneous data resources …

We propose a method to reconstruct global human trajectories from videos in the wild. Our
optimization method decouples the camera and human motion, which allows us to place …