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 …

In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset.
Existing motion datasets predominantly contain body-only poses, lacking facial expressions …

Whole-body pose estimation localizes the human body, hand, face, and foot keypoints in an
image. This task is challenging due to multi-scale body parts, fine-grained localization for …

Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion
capture with numerous applications. Despite encouraging progress, current state-of-the-art …

We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to
combine with the segment anything model (SAM). This integration enables the detection and …

We introduce ChatPose a framework employing Large Language Models (LLMs) to
understand and reason about 3D human poses from images or textual descriptions. Our …

In this work we present Digital Life Project a framework utilizing language as the universal
medium to build autonomous 3D characters who are capable of engaging in social …

The estimation of 3D human motion from video has progressed rapidly but current methods
still have several key limitations. First most methods estimate the human in camera …

Facial expression and hand motions are necessary to express our emotions and interact
with the world. Nevertheless, most of the 3D human avatars modeled from a casually …

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 …