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/cvg/Hierarchical-Localization
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/cviaai/PSF-GAN-ESTIMATION
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/cvlab-epfl/wide-depth-range-pose
https://github.com/cwbabel/faster-cnn
https://github.com/cxt98/Densefusion-transparency
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
https://github.com/d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/dahliau/DPDist
PCRNet: Point Cloud Registration Network using PointNet Encoding
https://github.com/dajes/DensePose-TorchScript
https://github.com/dalexanderch/CFD
https://github.com/danielperezr88/multiposenet-aries
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
https://github.com/danny1984/hourglass_keras
https://github.com/darkAlert/vibe-rt
VIBE: Video Inference for Human Body Pose and Shape Estimation
https://github.com/dc3ea9f/RegionViT
RegionViT: Regional-to-Local Attention for Vision Transformers
https://github.com/dcvl-3d/instancehmr_release
https://github.com/debjani-bhowmick/harmonic-net-pytorch
https://github.com/deepak112/Keras-SRGAN
https://github.com/deeplabcut/madlc_naturemethods2022
https://github.com/deepmodeling/Uni-Mol
Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction
https://github.com/delirecs/facial-keypoints-heatmaps
https://github.com/delldu/MaskRCNN
https://github.com/denilson020898/baseline_3d_pose
A simple yet effective baseline for 3d human pose estimation
https://github.com/deolipankaj/Stone_Detection_MRCNN
https://github.com/devsoft123/fast-cnn
https://github.com/dheerajpreddy/CenterNet-without-DCN
https://github.com/diamondto/IBP
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
https://github.com/digital-thinking/deep-posemachine
https://github.com/dineshreddy91/Occlusion_Net
https://github.com/dizhongzhu/Humanbody_Literatures
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
https://github.com/djsinghnegi/SRINImageEnhancemrntGAN
https://github.com/dlinzner-bcs/min_neuralODEs
https://github.com/doantienthongbku/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/dog-qiuqiu/MobileNetV2-SimplePose
https://github.com/dog-qiuqiu/Ultralight-SimplePose
https://github.com/doheelab/pku-CenterNet
https://github.com/dokyeongK/Pytorch_SRCNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.