A Comprehensive Review of Real-Time Multi-View Multi-Person Markerless Motion Capture

Abstract

Markerless human body motion capture promises to remove markers from capture studios, thus simplifying its diverse application fields, from life science to virtual reality. This comprehensive review examines recent advances in real-time markerless motion capture systems from 2020 to 2024, focusing on real-time multi-view, multi-person tracking solutions. Recent advancements, particularly driven by neural network-based pose estimation, have enabled real-time tracking with minimal latency, achieving at least 25 frames per second. Through systematic analysis, we evaluate these methods based on three key metrics: accuracy in pose reconstruction, end-to-end latency, and computational efficiency. Special attention is given to how architectural decisions impact system scalability regarding the number of camera viewpoints and tracked individuals. While current methods show promise for applications like sports analysis and virtual reality, challenges remain in achieving optimal performance across all metrics. Through systematic analysis of leading real-time pipelines, we identify key technical advances and persistent challenges. This synthesis provides critical insights for researchers and practitioners working to develop more robust markerless motion capture systems, while outlining important directions for future research.