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/twhui/SRGAN-PyTorch
https://github.com/txyugood/PaddlePose
https://github.com/txyugood/PaddlePoseC3D
https://github.com/tyIceStream/lightweight-human-pose-estimation.OpenCV
Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose
https://github.com/tyshiwo/DRRN_CVPR17
https://github.com/tzodge/PCR-CMU
PCRNet: Point Cloud Registration Network using PointNet Encoding
https://github.com/tzole1155/EndToEndObjectPose
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization
https://github.com/tzvikif/SuperGlue
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/u7javed/Image-Enhancer-via-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/u7javed/Image-Super-Resolution-Enhancer-via-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/ubc-vision/DwNet
https://github.com/ucbdrive/3d-vehicle-tracking
https://github.com/ucsd-ml-arts/ml-art-final2-phases
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/ucuapps/openglue
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/ultralytics/ultralytics
https://github.com/um-arm-lab/pytorch_mppi
https://github.com/umariqb/posetrack-cvpr2017
https://github.com/umich-vl/pose-hg-demo
https://github.com/umich-vl/pose-hg-train
https://github.com/ut-austin-rpl/forge
Few-View Object Reconstruction with Unknown Categories and Camera Poses
https://github.com/ut-vision/actionvos
Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose Estimation
https://github.com/v1viswan/Domain_adaptation_in_HRNet
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/vanoracai/Exploiting-Spatial-temporal-Relationships-for-3D-Pose-Estimation-via-Graph-Convolutional-Networks
https://github.com/varunvprabhu/simple_pose_hourglass
https://github.com/vdvchen/sgmnet
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/verityw/manipulation-final-project
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
https://github.com/verlab/accelerated_features
https://github.com/victimsnino/Simple-Baselines-for-Human-Pose-Estimation-and-Tracking-sample-
https://github.com/victimsnino/pose-simple-baselines-demo.pytorch.
https://github.com/viet-hoang-99/Lite_HRnet_vh
https://github.com/vietanhdev/centernet-bdd-data
https://github.com/vietanhdev/centernet-bdd-data-onnx-conversion
https://github.com/vietanhdev/open-adas
https://github.com/vietanhdev/tf-blazepose
https://github.com/vimalabs/VIMA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.