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/nannau/DoWnGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/nanzhaogang/contrib/tree/master/application/weakly-supervised-mesh-convolutional-hand-reconstruction-in-the-wild
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
https://github.com/narock/machine_learning
https://github.com/natepuppy/HCN-pytorch
https://github.com/naver/debit
End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon
https://github.com/naver/dope
DOPE: Distillation Of Part Experts for whole-body 3D pose estimation in the wild
https://github.com/naver/mast3r
https://github.com/naviocean/CenterNet
https://github.com/neherh/HyperStackNet
https://github.com/nerminsamet/houghnet-vid
HoughNet: Integrating near and long-range evidence for visual detection
https://github.com/niazwazir/REAL_TIME_VIDEO_IMSR
https://github.com/nickgkan/3d_diffuser_actor
Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
https://github.com/nihaomiao/MICCAI20_SiamParseNet
Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment
https://github.com/nihui/realsr-ncnn-vulkan
https://github.com/nikhithakarennagari/1311
https://github.com/nikhithakarennagari/Git
https://github.com/nilseuropa/ros_img2pose
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
https://github.com/nish-97v/3D-human-pose-estimation
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
https://github.com/nkolot/GraphCMR
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
https://github.com/nkolot/SPIN
Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop
https://github.com/nntanaka/Fourier-Analysis-for-Skeleton-based-Action-Recognition
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/nodarsensor/medium-keypoint-autocal
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/noelcodes/Mask_RCNN
https://github.com/notha99y/mean_teacher_domain_adapt
https://github.com/npielawski/pytorch_tiramisu
https://github.com/nsarafianos/Garment3DGen
https://github.com/nsubbaian/FrequentistML
https://github.com/ntoussaint/fetalnav
https://github.com/ntoussaint/qmedbrowser
https://github.com/nv-nguyen/pizza
https://github.com/nvidiagameworks/kaolin-wisp
https://github.com/nvlabs/dancing2music
https://github.com/o-evgeny/MRCNN_DeepFashion2
https://github.com/oarriaga/paz
https://github.com/olaviinha/NeuralImageSuperResolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.