15 citations · 19 across the 2 of their papers we have counts for
5 papers
PoseLifter: Absolute 3D human pose lifting network from a single noisy 2D human pose
Ju Yong Chang, Gyeongsik Moon, Kyoung Mu Lee
This study presents a new network (i.e., PoseLifter) that can lift a 2D human pose to an absolute 3D pose in a camera coordinate system. The proposed network estimates the absolute…
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee
Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case. In this work, we firstly prop…
Multi-scale Aggregation R-CNN for 2D Multi-person Pose Estimation
Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee
Multi-person pose estimation from a 2D image is challenging because it requires not only keypoint localization but also human detection. In state-of-the-art top-down methods, multi…
PoseFix: Model-agnostic General Human Pose Refinement Network
Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee
Multi-person pose estimation from a 2D image is an essential technique for human behavior understanding. In this paper, we propose a human pose refinement network that estimates a…
Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation
Gyeongsik Moon, Ju Yong Chang, Yumin Suh +1
We propose a novel approach to 3D human pose estimation from a single depth map. Recently, convolutional neural network (CNN) has become a powerful paradigm in computer vision. Man…