Papers with Code 아카이브
Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 31
- 보존된 평가 기록
- 235
Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.
A common benchmark for this task is MPII Human Pose
<span style="color:grey; opacity: 0.6">( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Guanghan/mxnet-centernet
https://github.com/GuillaumeDufau/Image-super-resolution-vdsr
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/GuohongLi/face-alignment-pytorch
https://github.com/HERMINDERSINGH1234/ML_Extra_Resolution_Increases
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/HRNet/HRNet-FCOS
High-Resolution Representations for Labeling Pixels and Regions
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/HajimeTaira/InLoc_demo
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
https://github.com/Harrypotterrrr/EDVR
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/HeatherJiaZG/SuperGlue-pytorch
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/Hem7513/Decision-Trees-and-XGBoost-Algorithm-Documentation
https://github.com/HighVoltageRocknRoll/sr
https://github.com/Hikaylee/SynergyNet
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
https://github.com/Hmslab/Adapose
https://github.com/HolmesShuan/OISR-PyTorch
https://github.com/HosseinEbrahimiK/Normalizing-Flows-Reproducing
https://github.com/HosseinEbrahimiK/TrajectoryNet-Reproducing
https://github.com/HritwikEkade/Image-Resolution
https://github.com/HungryCookie/VisDrone_CenterNet
https://github.com/HyeongJu916/Boaz-SR-ESRGAN-PyTorch
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/IDEA-Research/detrex/blob/main/projects/h_deformable_detr/README.md
https://github.com/IRVLab/funie-gan
Fast Underwater Image Enhancement for Improved Visual Perception
https://github.com/IVRL/MulT
https://github.com/Insaniauser/ViPNAS_mindspore
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search
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/JSeam2/Neural-Ordinary-Differential-Equations
https://github.com/Jacoo-Zhao/3D-Pose-Based-Feedback-For-Physical-Exercises
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/Jaekyu-Sim/Open_Pose_for_Naver
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/JakobGroeftehauge/BachelorProject
https://github.com/JakobGroeftehauge/Brick-Pose-Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.