AI-Driven Motion Capture Data Recovery: A Comprehensive Review and Future Outlook

Area of Science:

• Computer Vision
• Artificial Intelligence
• Data Science

Background:

• Motion capture (MoCap) data is crucial for various applications but is susceptible to missing or corrupted information.
• Existing recovery methods include non-data-driven, data-driven (AI-based), and hybrid approaches.
• AI techniques offer advanced capabilities but face limitations in computational demands and data requirements.

Purpose of the Study:

• To provide a comprehensive review of motion capture data recovery techniques.
• To specifically evaluate the suitability of artificial intelligence (AI) for MoCap data recovery.
• To synthesize insights across datasets, metrics, and failure cases for future research.

Main Methods:

• Classification of existing MoCap recovery methods into non-data-driven, AI-based, and hybrid categories.
• Review of AI frameworks like generative adversarial networks (GANs), transformers, and graph neural networks (GNNs).
• Examination of benchmark datasets (CMU MoCap, Human3.6M) and the role of synthetic/augmented data.

Main Results:

• AI methods, including GANs, transformers, and GNNs, demonstrate strong performance in spatial-temporal modeling and motion reconstruction.
• Hybrid approaches balance efficiency, interpretability, and robustness by integrating AI with traditional algorithms.
• AI model generalization is enhanced by synthetic and augmented data, yet hindered by a lack of standardized protocols and diverse real-world datasets.

Conclusions:

• AI-driven techniques represent a significant advancement in MoCap data recovery, offering superior adaptability and precision.
• Hybrid methods provide a practical compromise, addressing limitations of purely AI-based or traditional approaches.
• Future research should focus on real-time recovery, multimodal data fusion, and standardized benchmarks to overcome current challenges and improve generalization.