3D Pose Estimation
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Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision , ECCV'20
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zczcwh/DL-HPE
공식 코드https://github.com/zhangboshen/A2J
공식 코드A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image
https://github.com/zhuyue0324/decanus-to-legatus
공식 코드Decanus to Legatus: Synthetic training for 2D-3D human pose lifting
https://github.com/zqyq/Unsynchronized-Multi-Camera-Surveillance---IEEE-TNNLS
공식 코드Single-Frame based Deep View Synchronization for Unsynchronized Multi-Camera Surveillance
https://github.com/1996scarlet/dense-head-pose-estimation
https://github.com/2xic/openpose-pigs
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
https://github.com/AimAlex/Multi-view-3D-Human-Pose
A generalizable approach for multi-view 3D human pose regression
https://github.com/CASIA-IVA-Lab/ISP-reID
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/Chris10M/Ev2Hands
3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera
https://github.com/Daniil-Osokin/lightweight-human-pose-estimation-3d-demo.pytorch
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
https://github.com/DenisTome/Lifting-from-the-Deep-release
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/HRNet/HRNet-Facial-Landmark-Detection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Human-Pose-Estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Image-Classification
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-MaskRCNN-Benchmark
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Object-Detection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Semantic-Segmentation
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/JDAI-CV/DSD-SATN
Human Mesh Recovery from Monocular Images via a Skeleton-disentangled Representation
https://github.com/JHUVisionLab/multi-modal-regression
A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images
https://github.com/JiangWenPL/multiperson
Coherent Reconstruction of Multiple Humans from a Single Image
https://github.com/Mary-xl/HRnet_Kaggle_iNat2019_FGVC
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/ModelBunker/Lightweight-OpenPose-PyTorch
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
https://github.com/NU-LL/lighttrack-
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/NVlabs/PAMTRI
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/PaddlePaddle/PaddleDetection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/SJTU-DL-lab/3d-pose-baseline
A simple yet effective baseline for 3d human pose estimation
https://github.com/ShawnHXH/GNN-CCA
https://github.com/SyBorg91/pose-estimation-detection
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/SyedUmaidAhmed/Pose-Detection-Sitting-Standing
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/VCL3D/DronePose
https://github.com/VCL3D/UAVA
https://github.com/VNOpenAI/tf-blazepose
https://github.com/Varun-Tandon14/Implementation-of-Cross-View-Tracking-for-Multi-Human-3D-Pose-Estimation-at-over-100-FPS
Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS
https://github.com/Vill-Lab/2022-TIP-HCGA
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/Watson-BCA/HumanPostureRecognition
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.