https://github.com/judyye/ihoi
What's in your hands? 3D Reconstruction of Generic Objects in Hands
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/jupiterman/Super-Resolution-Images
https://github.com/jyhengcoder/Triplet-Attention-tf
https://github.com/jylins/core-text
https://github.com/k-miran/hear
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/kadirnar/bsrgan-pip
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
https://github.com/kai422/iart
Enhancing Video Super-Resolution via Implicit Resampling-based Alignment
https://github.com/kaijieshi7/Dynamic-convolution-Pytorch
https://github.com/kakao/DAFT
https://github.com/karanshahgithub/CS256-AI-Pose2Mesh
Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose
https://github.com/karaposu/facexformer-pipeline
https://github.com/karfly/learnable-triangulation-pytorch
https://github.com/karlgodin/DP10-hockey-pose-estimation
https://github.com/karshetty/pliks
PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation
https://github.com/kaulquappe23/a2b_human_mesh
Uplift and Upsample: Efficient 3D Human Pose Estimation with Uplifting Transformers
https://github.com/kbardool/mrcnn3
https://github.com/kdethoor/panoptictorch
https://github.com/kdha0727/cancer-instance-segmentation-from-tissue
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/kdhingra307/temp
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/ken-power/SensorFusionND-3D-Object-Tracking
https://github.com/ken724049/action-recognition
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/kennethwdk/cid
https://github.com/kennymckormick/pyskl
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/kfallah/NODE-Denoiser
https://github.com/khai9xht/Multitask_head_pose
Simultaneous face detection and 360 degree headpose estimation
https://github.com/khushgrover/smart-match
https://github.com/khyeyoon/Video-Super-Resolution
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/kieran514/baseboostdepth
BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth Estimation
https://github.com/kieran514/robustdepth
Self-supervised Monocular Depth Estimation: Let's Talk About The Weather
https://github.com/kimsinjeong/keypt2subpx
Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task
https://github.com/kingcheng2000/GAN
https://github.com/kingcong/HRNetW48_seg
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/kingcong/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/kingcong/gpu_ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/kingcong/gpu_HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/kingcong/gpu_RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.