International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Comparative Evaluation of AI Driven Markerless Motion Capture Tools for Efficiency

  • Balgum Song (Department of International College, Dongseo University)
  • Received : 2024.09.15
  • Accepted : 2024.09.26
  • Published : 2024.11.30
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Abstract

We explore the effectiveness of AI-driven markerless motion capture (MoCap) tools compared to the traditional marker-based OptiTrack system, known for its high accuracy in capturing precise movements. Through detailed comparative analysis, we assessed various free markerless MoCap tools, including Move One, Radical, Deep Motion, Plask, Rokoko, and Movmi, focusing on critical aspects such as pose accuracy, movement smoothness, and ground detection. Our findings indicate that Move One is the most versatile tool, offering excellent pose accuracy, smooth MoCap, and reliable ground detection, making it a strong contender for various animation tasks. We found that Radical excels in minimizing jitter, making it suitable for projects requiring smooth motion, while Deep Motion performs best in ground detection, which is crucial for accurate foot placement. Although markerless systems still do not fully match the precision of marker-based systems, we suggest that they present viable alternatives depending on the specific needs of a project. As AI technology continues to advance, we expect the gap between markerless and marker-based to narrow, expanding the potential applications of markerless MoCap in the industry.

Keywords

Acknowledgement

This work was supported by Dongseo University, "Dongseo Frontier Project" Research Fund of 2023

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