https://github.com/CMU-Perceptual-Computing-Lab/openpose_train
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Multi-person pose estimation is the task of estimating the pose of multiple people in one frame.
<span style="color:grey; opacity: 0.6">( Image credit: Human Pose Estimation with TensorFlow )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose
Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Deep High-Resolution Representation Learning for Human Pose Estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Distribution-Aware Coordinate Representation for Human Pose Estimation
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.