https://github.com/oli4jansen/VIBE_old
VIBE: Video Inference for Human Body Pose and Shape Estimation
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/omglu93/energy_consumption_eda_prediction
https://github.com/omkarbhope/Pose-Estimation
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
https://github.com/omkarghugarkar007/Neural_Super_Sampling
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/open-catalyst-project/adsorbml
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
https://github.com/open-edge-platform/geti
https://github.com/open-edge-platform/training_extensions
https://github.com/open-mmlab/mmaction2
https://github.com/open-mmlab/mmclassification
https://github.com/open-mmlab/mmdetection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/README.md
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/open-mmlab/mmsegmentation
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/open-mmlab/mmskeleton
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/open-mmlab/mmsr
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/openpifpaf/openpifpaf
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
https://github.com/openpifpaf/openpifpaf_posetrack
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
https://github.com/openpifpaf/openpifpafwebdemo
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
https://github.com/openxrlab/xrmocap
Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views
https://github.com/orkqueen/depplabseongil
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/osmr/imgclsmob
https://github.com/osofr/xgboost
https://github.com/ostadabbas/Seeing-Under-the-Cover
Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation
https://github.com/ostadabbas/in-bed-pose-estimation
https://github.com/otaheri/GOAL
https://github.com/otaheri/GrabNet
https://github.com/oublalkhalid/AI-OpenPose
https://github.com/owena11/papers
https://github.com/pEAceATLast53/Social-Navigation
Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning
https://github.com/pairlab/6pack
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
https://github.com/paplhjak/facial-age-estimation-benchmark
https://github.com/paradisetechsoftsolutions/Deep-learning-model-using-Mask-R-CNN-for-predicting-AC-remote
https://github.com/parthapratimbanik/facial-keypoint-detection-udacity-ppb
https://github.com/pasrichashivam/hyperface_deep_multi-task-learning_keras_implementation
https://github.com/patiencefromzhou/simplehand
https://github.com/patrick-kidger/FasterNeuralDiffEq
https://github.com/paulmthompson/StackedHourglass.jl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.