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 3D humans from images often produces implausible bodies that lean, float, or
penetrate the floor. Such methods ignore the fact that bodies are typically supported by the …

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

Top-down methods for monocular human mesh recovery have two stages:(1) detect human
bounding boxes;(2) treat each bounding box as an independent single-human mesh …

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 …

When analyzing human motion videos, the output jitters from existing pose estimators are
highly-unbalanced with varied estimation errors across frames. Most frames in a video are …

In this study, we focus on the problem of 3D human mesh recovery from a single image
under obscured conditions. Most state-of-the-art methods aim to improve 2D alignment …

The state-of-the-art for monocular 3D human pose estimation in videos is dominated by the
paradigm of 2D-to-3D pose uplifting. While the uplifting methods themselves are rather …

The advances in monocular 3D human pose estimation are dominated by supervised
techniques that require large-scale 2D/3D pose annotations. Such methods often behave …

In this paper, we introduce the new task of reconstructing 3D human pose from a single
image in which we can see the person and the person's image through a mirror. Compared …