Human pose estimation aims to locate the human body parts and build human body
representation (eg, body skeleton) from input data such as images and videos. It has drawn …

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 …

Top-down methods dominate the field of 3D human pose and shape estimation, because
they are decoupled from human detection and allow researchers to focus on the core …

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 …

In this work, we tackle the problem of learning universal robotic dexterous grasping from a
point cloud observation under a table-top setting. The goal is to grasp and lift up objects in …

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 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 …

Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose,
a versatile framework to model plausible 3D human poses for various applications. At the …