CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark

2018-12-02 · CVPR 2019 6 · Jiefeng Li, Can Wang, Hao Zhu, Yihuan Mao, Hao-Shu Fang, Cewu Lu

Multi-person pose estimation is fundamental to many computer vision tasks and has made significant progress in recent years. However, few previous methods explored the problem of pose estimation in crowded scenes while it remains challenging and inevitable in many scenarios. Moreover, current benchmarks cannot provide an appropriate evaluation for such cases. In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms. Our model consists of two key components: joint-candidate single person pose estimation (SPPE) and global maximum joints association. With multi-peak prediction for each joint and global association using graph model, our method is robust to inevitable interference in crowded scenes and very efficient in inference. The proposed method surpasses the state-of-the-art methods on CrowdPose dataset by 5.2 mAP and results on MSCOCO dataset demonstrate the generalization ability of our method. Source code and dataset will be made publicly available.

📄 PDF Abstract BibTeX arXiv:1812.00324

Code (4)

Jeff-sjtu/CrowdPose pytorch

jeffffffli/CrowdPose pytorch

laowang666888/ECSP1 pytorch

open-mmlab/mmpose pytorch

Tasks

Keypoint Detection Multi-Person Pose Estimation Pose Estimation

Contextual Instance Decoupling for Robust Multi-Person Pose Estimation

2022-01-01 · CVPR 2022 1 · Dongkai Wang, Shiliang Zhang

Crowded scenes make it challenging to differentiate persons and locate their pose keypoints. This paper proposes the Contextual Instance Decoupling (CID), which presents a new pipeline for multi-person pose estimatio…

Human Instance Segmentation Multi-Person Pose Estimation Pose Estimation

DiffusionRegPose: Enhancing Multi-Person Pose Estimation using a Diffusion-Based End-to-End Regression Approach

2024-01-01 · CVPR 2024 1 · Dayi Tan, Hansheng Chen, Wei Tian, Lu Xiong

This paper presents the DiffusionRegPose a novel approach to multi-person pose estimation that converts a one-stage end-to-end keypoint regression model into a diffusion-based sampling process. Existing one-stage det…

Multi-Person Pose Estimation Pose Estimation regression

BoIR: Box-Supervised Instance Representation for Multi-Person Pose Estimation

2023-09-25 · Uyoung Jeong, Seungryul Baek, Hyung Jin Chang, Kwang In Kim

Single-stage multi-person human pose estimation (MPPE) methods have shown great performance improvements, but existing methods fail to disentangle features by individual instances under crowded scenes. In this paper, we …

Disentanglement Keypoint Estimation Multi-Person Pose Estimation Multi-Task Learning+2

Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation

2021-01-27 · ICCV 2021 10 · Rawal Khirodkar, Visesh Chari, Amit Agrawal, Ambrish Tyagi

A key assumption of top-down human pose estimation approaches is their expectation of having a single person/instance present in the input bounding box. This often leads to failures in crowded scenes with occlusions. We …

2D Human Pose Estimation Keypoint Detection Multi-Person Pose Estimation Pose Estimation

BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations

2021-12-20 · Bruno Artacho, Andreas Savakis

We propose BAPose, a novel bottom-up approach that achieves state-of-the-art results for multi-person pose estimation. Our end-to-end trainable framework leverages a disentangled multi-scale waterfall architecture and in…

Multi-Person Pose Estimation Pose Estimation