[TIP 2022] Boosting Monocular 3D Human Pose Estimation with Part Aware Attention
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Updated
Sep 28, 2022 - Python
[TIP 2022] Boosting Monocular 3D Human Pose Estimation with Part Aware Attention
[AAAI 2024] PoseGen: Learning to Generate 3D Human Pose Datasets with NeRF
Pytorch implementation of 3D human keypoints estimation models
We introduce a new 3D pose estimator and model designed for runners.
Change "Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image" to video input implementation
3D human pose and ground truth viewpoint datasets used in the paper "Optimal Camera Point Selection Toward the Most Preferable View of 3-D Human Pose"
Human Pose annotation tool. With this tool it is possible to annotate custom 2D skeletons over images or videos. If 3D skeletons are also available, there is also a 3D visualization.
This is a pytorch implementation of method based on Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation applying on human pose estimation tasks using 2-view stereo images.
Official implementation of ACCV 2020 paper "3D Human Motion Estimation via Motion Compression and Refinement". (Identical repo to https://github.com/ZhengyiLuo/MEVA, will be kept in sync)
Python script for rendering 3D human poses using Blender
Official repository of PAFUSE
3D Multi-person Pose Estimation in Multi-view Environment using 3D U-Net Transformer Networks
[CVPR 2023] Code repository for HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation
[CVPR 2022 Oral] Generalizable Human Pose Triangulation
PyTorch code for EgoHMR (ICCV 2023): Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric Views
A real-time 3d human pose estimation trial.
Kinematic-aware Hierarchical Attention Network for Human Pose Estimation in Videos (WACV 2023)
Estimating 3d Landmarks and displaying them in rviz (Cpu)
A toolbox for processing Total Capture dataset
[CVPR 2023 Highlight] Official implementation of "NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action"
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