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Sign in the Air to Unlock: An Interface for authentication in Virtual and Augmented Reality Powered by Point-Voxel Cross-Attention Network

Researchers propose 'Sign in the Air to Unlock', an in-air signature interface for VR/AR authentication using a Point-Voxel Cross-Attention Network (PV-Net). It enables natural signing in 3D space without breaking immersion or requiring specialized sensors. PV-Net achieves 2.5% Equal Error Rate on DeepAirSig and 76% classification accuracy on a new Meta Quest 2 dataset, demonstrating the potential of 3D behavioral authentication.

SourcearXiv Computer VisionAuthor: Neda Abdolrahimi, Thiru Siddharth, Frank Sicongchen, Vir V Phoha

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[Submitted on 1 Jul 2026]

Title:Sign in the Air to Unlock: An Interface for authentication in Virtual and Augmented Reality Powered by Point-Voxel Cross-Attention Network

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Abstract:Significant advancement of immersive technologies such as Virtual and Augmented Reality (VR/AR) and their integration into diverse aspects of modern life need authentication interfaces that are secure, intuitive, and compatible with embodied interaction. Traditional methods such as passwords, PINs, and device-based logins, break immersion and rely on external hardware. Recent 3D-specific behavioral approaches, such as hand-gesture, eye-tracking, and electroencephalography (EEG)-based methods, offer promising alternatives but often require specialized sensors or constrain natural movement, limiting usability in dynamic environments. We present Sign in the Air to Unlock, an in-air signature interface that enables users to authenticate by signing naturally in 3D space which is a familiar, personal, and reproducible gesture. To realize this interface, we design a point-voxel Cross-Attention Network (PV-Net) that jointly models local motion dynamics and global spatial structure from 3D trajectories. The model is evaluated on two datasets: the public DeepAirSig dataset (1,800 signatures from 40 users) and ImmAirsig, a new dataset collected using Meta Quest 2 in immersive VR (880 samples from 22 users). PV-Net achieves an Equal Error Rate of 2.5% on DeepAirSig and 76% classification accuracy on ImmAirSig. These findings highlight the potential of 3D behavioral interfaces for seamless, user-centric authentication that merges security with natural interaction in immersive environments.

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)

Cite as: arXiv:2607.01435 [cs.CV]

(or arXiv:2607.01435v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.01435

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Thiru Siddharth [view email] [v1] Wed, 1 Jul 2026 19:56:55 UTC (3,743 KB)

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