8 citations · 10 across the 5 of their papers we have counts for
5 papers · 1 filter
BEDLAM2.0: Synthetic Humans and Cameras in Motion
Joachim Tesch, Giorgio Becherini, Prerana Achar +4
Inferring 3D human motion from video remains a challenging problem with many applications. While traditional methods estimate the human in image coordinates, many applications requ…
Toward Human Understanding with Controllable Synthesis
Hanz Cuevas-Velasquez, Priyanka Patel, Haiwen Feng +1
Training methods to perform robust 3D human pose and shape (HPS) estimation requires diverse training images with accurate ground truth. While BEDLAM demonstrates the potential of…
CameraHMR: Aligning People with Perspective
Priyanka Patel, Michael J. Black
We address the challenge of accurate 3D human pose and shape estimation from monocular images. The key to accuracy and robustness lies in high-quality training data. Existing train…
TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation
Sai Kumar Dwivedi, Yu Sun, Priyanka Patel +2
We address the problem of regressing 3D human pose and shape from a single image, with a focus on 3D accuracy. The current best methods leverage large datasets of 3D pseudo-ground-…
ChatPose: Chatting about 3D Human Pose
Yao Feng, Jing Lin, Sai Kumar Dwivedi +3
We introduce ChatPose, a framework employing Large Language Models (LLMs) to understand and reason about 3D human poses from images or textual descriptions. Our work is motivated b…