Multi-Person Pose Estimation
81 papers with code • 11 benchmarks • 7 datasets
Multi-person pose estimation is the task of estimating the pose of multiple people in one frame.
( Image credit: Human Pose Estimation with TensorFlow )
Libraries
Use these libraries to find Multi-Person Pose Estimation models and implementationsLatest papers
RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimation
Real-time multi-person pose estimation presents significant challenges in balancing speed and precision.
UniPose: Detecting Any Keypoints
This work proposes a unified framework called UniPose to detect keypoints of any articulated (e. g., human and animal), rigid, and soft objects via visual or textual prompts for fine-grained vision understanding and manipulation.
BoIR: Box-Supervised Instance Representation for Multi-Person Pose Estimation
Our new instance embedding loss provides a learning signal on the entire area of the image with bounding box annotations, achieving globally consistent and disentangled instance representation.
Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation
State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e. g., regarding pose estimation as keypoint box detection and combining with human detection in ED-Pose, hierarchically predicting with pose decoder and joint (keypoint) decoder in PETR.
LAMP: Leveraging Language Prompts for Multi-person Pose Estimation
Human-centric visual understanding is an important desideratum for effective human-robot interaction.
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity
Frequent interactions between individuals are a fundamental challenge for pose estimation algorithms.
A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing
To this end, we deployed an end-to-end edge computing pipeline that utilizes multiple cameras to achieve localization, body orientation estimation and tracking of multiple individuals within a large therapeutic space spanning $1700m^2$, all while maintaining a strong focus on preserving privacy.
RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose
Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency.
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information.
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision.