https://github.com/3dpose/3D-Multi-Person-Pose
공식 코드Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks
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.
Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks
Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
PoseDet: Fast Multi-Person Pose Estimation Using Pose Embedding
Human Pose Regression with Residual Log-likelihood Estimation
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
Deep High-Resolution Representation Learning for Human Pose Estimation
DETRPose: Real-time end-to-end transformer model for multi-person pose estimation
Alleviating Human-level Shift : A Robust Domain Adaptation Method for Multi-person Pose Estimation
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity
Unifying Part Detection and Association for Recurrent Multi-Person Pose Estimation
AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression
Learning Delicate Local Representations for Multi-Person Pose Estimation
Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation
The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation
InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
Metric-Scale Truncation-Robust Heatmaps for 3D Human Pose Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.