Selecting the motion ground truth for loose-fitting wearables: benchmarking optical MoCap methods
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Abstract (arXiv)
To help smart wearable researchers choose the optimal ground truth methods for motion capturing (MoCap) for all types of loose garments, we present a benchmark, DrapeMoCapBench (DMCB), specifically designed to evaluate the performance of optical marker-based and marker-less MoCap. High-cost marker-based MoCap systems are well-known as precise golden standards. However, a less well-known caveat is that they require skin-tight fitting markers on bony areas to ensure the specified precision, making them questionable for loose garments. On the other hand, marker-less MoCap methods powered by computer vision models have matured over the years, which have meager costs as smartphone cameras would suffice. To this end, DMCB uses large real-world recorded MoCap datasets to perform parallel 3D physics simulations with a wide range of diversities: six levels of drape from skin-tight to extremely draped garments, three levels of motions and six body type - gender combinations to benchmark state-of-the-art optical marker-based and marker-less MoCap methods to identify the best-performing method in different scenarios. In assessing the performance of marker-based and low-cost marker-less MoCap for casual loose garments both approaches exhibit significant performance loss (>10cm), but for everyday activities involving basic and fast motions, marker-less MoCap slightly outperforms marker-based MoCap, making it a favorable and cost-effective choice for wearable studies.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 11 of its 53 references that are in the map. First posted on arXiv 21 Jul 2023.
Builds on
11 of 53 references.
References · 11
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- 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,236
- 2017End-to-end Recovery of Human Shape and Posecited by 885
- 20183D human pose estimation in video with temporal convolutions and semi-supervised trainingcited by 403
- 2022Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensorscited by 146
- 2020BlazePose: On-device Real-time Body Pose trackingcited by 113
- 2019Cross View Fusion for 3D Human Pose Estimationcited by 90
- 2022Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generationcited by 76
- 2022IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Textcited by 37
- 2023PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulationcited by 9
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2024SeamPose: Repurposing Seams as Capacitive Sensors in a Shirt for Upper-Body Pose Trackingcited by 3
- 2025ChairPose: Pressure-based Chair Morphology Grounded Sitting Pose Estimation through Simulation-Assisted Trainingcited by 0
- 2025VersaPants: A Loose-Fitting Textile Capacitive Sensing System for Lower-Body Motion Capturecited by 0
- 2025VersaPants: A Loose-Fitting Textile Capacitive Sensing System for Lower-Body Motion Capture20 Nov 2025
- 2025ChairPose: Pressure-based Chair Morphology Grounded Sitting Pose Estimation through Simulation-Assisted Training3 Aug 2025
- 2024SeamPose: Repurposing Seams as Capacitive Sensors in a Shirt for Upper-Body Pose Tracking17 Jun 2024