Estimating 3D human poses from monocular videos is a challenging task due to depth
ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting …

Recent transformer-based solutions have been introduced to estimate 3D human pose from
2D keypoint sequence by considering body joints among all frames globally to learn spatio …

Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity
and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand …

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 …

Abstract 3D human pose estimation has been researched for decades with promising fruits.
3D human pose lifting is one of the promising research directions toward the task where …

While the voxel-based methods have achieved promising results for multi-person 3D pose
estimation from multi-cameras, they suffer from heavy computation burdens, especially for …

We present VoxelTrack for multi-person 3D pose estimation and tracking from a few
cameras which are separated by wide baselines. It employs a multi-branch network to jointly …

Depth ambiguity is one of the main challenges of three-dimensional (3D) human pose
estimation (HPE). The recent strategies of disambiguating have brought significant progress …

Autonomous driving is an exciting new industry, posing important research questions. Within
the perception module, 3D human pose estimation is an emerging technology, which can …

The dominant paradigm in 3D human pose estimation that lifts a 2D pose sequence to 3D
heavily relies on long-term temporal clues (ie, using a daunting number of video frames) for …