36 citations · 73 across the 11 of their papers we have counts for
13 papers
Stable Diffusion-Based Approach for Human De-Occlusion
Seung Young Noh, Ju Yong Chang
Humans can infer the missing parts of an occluded object by leveraging prior knowledge and visible cues. However, enabling deep learning models to accurately predict such occluded…
Bidirectional Regression for Monocular 6DoF Head Pose Estimation and Reference System Alignment
Sungho Chun, Boeun Kim, Hyung Jin Chang +1
Precise six-degree-of-freedom (6DoF) head pose estimation is crucial for safety-critical applications and human-computer interaction scenarios, yet existing monocular methods still…
Representation learning of vertex heatmaps for 3D human mesh reconstruction from multi-view images
Sungho Chun, Sungbum Park, Ju Yong Chang
This study addresses the problem of 3D human mesh reconstruction from multi-view images. Recently, approaches that directly estimate the skinned multi-person linear model (SMPL)-ba…
Learnable human mesh triangulation for 3D human pose and shape estimation
Sungho Chun, Sungbum Park, Ju Yong Chang
Compared to joint position, the accuracy of joint rotation and shape estimation has received relatively little attention in the skinned multi-person linear model (SMPL)-based human…
Camera Motion Agnostic 3D Human Pose Estimation
Seong Hyun Kim, Sunwon Jeong, Sungbum Park +1
Although the performance of 3D human pose and shape estimation methods has improved significantly in recent years, existing approaches typically generate 3D poses defined in camera…
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…
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Shanxin Yuan, Guillermo Garcia-Hernando, Bjorn Stenger +21
In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled?…
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee
Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directl…
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…
2D-3D Pose Consistency-based Conditional Random Fields for 3D Human Pose Estimation
Ju Yong Chang, Kyoung Mu Lee
This study considers the 3D human pose estimation problem in a single RGB image by proposing a conditional random field (CRF) model over 2D poses, in which the 3D pose is obtained…