Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integration

2026-08-03

AI Summary

This work addresses the limitations of existing multimodal sensor-based human motion capture methods, which heavily rely on cross-device calibration, are constrained by sensor placement, and suffer significant performance degradation under noise or partial sensor failure. To overcome these challenges, we propose a calibration-free LiDAR-camera fusion framework that achieves robust cross-modal alignment and motion estimation through body-centric pose reconstruction and iterative trajectory optimization. Our approach accommodates an arbitrary number and configuration of sensors, greatly enhancing deployment flexibility and environmental robustness. It delivers real-time, state-of-the-art performance on benchmarks such as Human-M3 and FreeMotion, while demonstrating strong cross-domain generalization capabilities on LiDARHuman26M and RELI11D.

Abstract

We propose Sen-Cap, a Sensor-Flexible and Noise-Resilient 3D human motion Capture framework that integrates multi-modal data from LiDAR and camera. While multi-modal sensors provide richer information than single-modal sensors, existing approaches still suffer from two core challenges. First, multi-modal alignment/matching across arbitrarily deployed sensors is typically handled by explicit calibration, which propagates errors under changing viewpoints and in turn constrains deployment to fixed, highly overlapped layouts. Second, prior methods degrade under severe noise or partial sensor failures, which are common in real-world environments. To address these challenges, Sen-Cap introduces a Unified Across-Sensor Motion Estimator that reconstructs local pose and shape in a human-centric space without calibrations between sensors, supporting a flexible number of sensors, as well as a Noise-Resistant Trajectory Tracker that maintains robustness under severe point cloud noise through iterative refinement. These sensor-flexible and noise-resilient features make Sen-Cap more practical in real-world deployment. Notably, operating in real time, Sen-Cap achieves state-of-the-art performance on major metrics on Human-M3 and FreeMotion, as well as strong cross-domain performance on LiDARHuman26M and RELI11D. This combination of flexibility and robustness opens new opportunities for motion capture in real-world scenarios, e.g. sports analytics, field robotics, and large-scale immersive environments.

Problem

• motion capture
• multi-modal sensor alignment
• sensor calibration
• noise robustness
• real-world deployment

Innovation

• sensor-flexible
• noise-resilient
• LiDAR-camera integration
• calibration-free motion capture
• robust human pose estimation