Search Results for author:
Found 12 papers, 6 papers with code
Representation learning of vertex heatmaps for 3D human mesh reconstruction from multi-view images
We show that representation learning of vertex heatmaps using an autoencoder helps improve the performance of such approaches.
Ranked #1 on
3D Human Pose Estimation
on Human3.6M
(using extra training data)
Learnable human mesh triangulation for 3D human pose and shape estimation
The proposed method first estimates the coordinates of mesh vertices through a CNN-based model from input images, and acquires SMPL parameters by fitting the SMPL model to the estimated vertices.
Ranked #3 on
3D Human Pose Estimation
on Human3.6M
Camera Motion Agnostic 3D Human Pose Estimation
To address this issue, this paper presents a camera motion agnostic approach for predicting 3D human pose and mesh defined in the world coordinate system.
Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video
Our TCMR significantly outperforms previous video-based methods in temporal consistency with better per-frame 3D pose and shape accuracy.
Ranked #58 on
3D Human Pose Estimation
on 3DPW
PoseLifter: Absolute 3D human pose lifting network from a single noisy 2D human pose
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.
Ranked #49 on
3D Human Pose Estimation
on MPI-INF-3DHP
(PCK metric)
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
Multi-scale Aggregation R-CNN for 2D Multi-person Pose Estimation
Multi-person pose estimation from a 2D image is challenging because it requires not only keypoint localization but also human detection.
PoseFix: Model-agnostic General Human Pose Refinement Network
In this paper, we propose a human pose refinement network that estimates a refined pose from a tuple of an input image and input pose.
Ranked #2 on
Multi-Person Pose Estimation
on MS COCO
(Validation AP metric)
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Official Torch7 implementation of "V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map", CVPR 2018
Ranked #5 on
Hand Pose Estimation
on HANDS 2017
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint.
Ranked #3 on
Pose Estimation
on ITOP front-view
Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation
We propose a novel approach to 3D human pose estimation from a single depth map.
2D-3D Pose Consistency-based Conditional Random Fields for 3D Human Pose Estimation
The unary term of the proposed CRF model is defined based on a powerful heat-map regression network, which has been proposed for 2D human pose estimation.
