Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views

Abstract

This paper addresses the problem of 3D pose estimation for multiple people in a few calibrated camera views. The main challenge of this problem is to find the cross-view correspondences among noisy and incomplete 2D pose predictions. Most previous methods address this challenge by directly reasoning in 3D using a pictorial structure model, which is inefficient due to the huge state space. We propose a fast and robust approach to solve this problem. Our key idea is to use a multi-way matching algorithm to cluster the detected 2D poses in all views. Each resulting cluster encodes 2D poses of the same person across different views and consistent correspondences across the keypoints, from which the 3D pose of each person can be effectively inferred. The proposed convex optimization based multi-way matching algorithm is efficient and robust against missing and false detections, without knowing the number of people in the scene. Moreover, we propose to combine geometric and appearance cues for cross-view matching. The proposed approach achieves significant performance gains from the state-of-the-art (96.3% vs. 90.6% and 96.9% vs. 88% on the Campus and Shelf datasets, respectively), while being efficient for real-time applications.

Project page: https://zju-3dv.github.io/mvpose/

References in corpus (18)

• DeepPose: Human Pose Estimation via Deep Neural Networks
• Associative Embedding: End-to-End Learning for Joint Detection and Grouping
• Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
• Stacked Hourglass Networks for Human Pose Estimation
• Convolutional Pose Machines
• Cascaded Pyramid Network for Multi-Person Pose Estimation
• A simple yet effective baseline for 3d human pose estimation
• Flowing ConvNets for Human Pose Estimation in Videos
• Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
• RMPE: Regional Multi-person Pose Estimation
• Compositional Human Pose Regression
• Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video
• DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
• Learning to Estimate 3D Human Pose and Shape from a Single Color Image
• Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation
• 3D Human Pose Estimation from a Single Image via Distance Matrix Regression
• Panoptic Studio: A Massively Multiview System for Social Interaction Capture
• Multi-Image Matching via Fast Alternating Minimization

Cited by in corpus (5)

• Real-Time Multi-View 3D Human Pose Estimation using Semantic Feedback to Smart Edge Sensors
• Single-Stage Multi-Person Pose Machines
• Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach
• 4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras
• Lightweight Multi-person Total Motion Capture Using Sparse Multi-view Cameras