Towards Real-World Wearable Motion Reconstruction
A paper accepted at ECCV 2026 presents a new approach to wearable motion capture that works with any combination of consumer devices like smartphones and smartwatches, introducing the WHIP model and a comprehensive dataset spanning 50 activities, along with a systematic study of sensor complementarity.
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[Submitted on 8 Jul 2026]
Title:Towards Real-World Wearable Motion Reconstruction
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Abstract:The modern-day surge in popularity of wearable devices poses a fundamentally unique motion capture problem: reconstructing full-body movement from any set of sensing hardware worn at a given moment. Yet, most research efforts assume fixed sensor configurations (e.g. IMU suits or HMD-centric rigs) and cannot generalize across them. In contrast, we argue that motion capture should prioritize unobtrusive and lightweight devices such as smartphones, smartwatches, smart glasses, and smart insoles, and study the interplay between them. To this end, we make three contributions. First, we present a large-scale multi-modal dataset synchronizing these consumer-grade sensors with ground-truth 3D motion, spanning 50 diverse activities including everyday tasks, sports, and social interactions. Second, we propose WHIP, a baseline generative model that reconstructs motion from arbitrary subsets of available sensors, robustly handling missing modalities and producing physically plausible motions. Third, we conduct a systematic study of sensor complementarity, quantifying how different modalities complement one another. Code and dataset are available at this https URL
Comments: Accepted at ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.09780 [cs.CV]
(or arXiv:2607.09780v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.09780
arXiv-issued DOI via DataCite
Submission history
From: Andrea Boscolo Camiletto [view email] [v1] Wed, 8 Jul 2026 11:56:02 UTC (1,751 KB)
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