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/starman360/openpose_xavier
https://github.com/stdbreaks/centernet
https://github.com/stefan-ainetter/scannotate
Automatically Annotating Indoor Images with CAD Models via RGB-D Scans
https://github.com/stevehjc/pose-ae-demo-tf
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
https://github.com/stillarrow/S2VT_ACT
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/stimong/DensePose_python3
https://github.com/strawberryfg/c2f-3dhm-human-caffe
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
https://github.com/strivebo/image_segmentation_dl
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/sudarsanGyrus/3D_Pose_VIBE
VIBE: Video Inference for Human Body Pose and Shape Estimation
https://github.com/sunhui1234/haha
https://github.com/sunqiangxtcsun/faster-rcnn
https://github.com/sunutf/TSM
https://github.com/suranakritika/Deep-pose
https://github.com/svikramank/DensePose
https://github.com/swathi-469/Teresa-Bot
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/t-hanya/kuzushiji-recognition
https://github.com/taeinkwon/CASA
Context-Aware Sequence Alignment using 4D Skeletal Augmentation
https://github.com/taeyoung96/gril-calib
https://github.com/takiyu/hyperface
https://github.com/tamasino52/Any-GCN
Semantic Graph Convolutional Networks for 3D Human Pose Regression
https://github.com/tamasino52/GraphSH
Graph Stacked Hourglass Networks for 3D Human Pose Estimation
https://github.com/tamasino52/U-CondDGCN
Conditional Directed Graph Convolution for 3D Human Pose Estimation
https://github.com/tamasino52/UGCN
https://github.com/tanmaysingha/2D-3D-FAN
https://github.com/tanreinama/XceptionHourgrass---PyTorch
https://github.com/tbd-clip/efficientpose
https://github.com/tea1528/Non-Local-NN-Pytorch
https://github.com/teakkkz/imageSR
https://github.com/techforgood-kiran/ai
https://github.com/teddykoker/unsupervised-deep-homography
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
https://github.com/tegg89/SRCNN-Tensorflow
https://github.com/tejaswigowda/semseg-pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/tensorboy/centerpose
https://github.com/tensorflow/models/tree/master/official/vision
https://github.com/tensorflow/models/tree/master/research/keypointnet
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.