A Low-Cost & Real-Time Motion Capture System

Anargyros Chatzitofis, Georgios Albanis, Nikolaos Zioulis, Spyridon Thermos; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 21453-21458

Abstract

Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption. In this work, we demonstrate such a system but rely on a very sparse set of low-cost consumer-grade sensors. Our system exploits a data-driven backend to infer the captured subject's joint positions from noisy marker estimates in real-time. In addition to reduced costs and portability, its inherent denoising nature allows for quicker captures by alleviating the need for precise marker placement and post-processing, making it suitable for interactive virtual reality applications.

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[bibtex]
@InProceedings{Chatzitofis_2022_CVPR, author = {Chatzitofis, Anargyros and Albanis, Georgios and Zioulis, Nikolaos and Thermos, Spyridon}, title = {A Low-Cost \& Real-Time Motion Capture System}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {21453-21458} }