Sign in the Air to Unlock: An Interface for authentication in Virtual and Augmented Reality Powered by Point-Voxel Cross-Attention Network
Quick Answer
This paper shows that The 'Sign in the Air to Unlock' interface utilizes a point-voxel Cross-Attention Network (PV-Net) for 3D signature authentication in VR/AR, achieving a 2.5% Equal Error Rate on the DeepAirSig dataset and 76% accuracy on ImmAirsig, enhancing user-centric security without disrupting immersion.
Quick Take
The 'Sign in the Air to Unlock' interface utilizes a point-voxel Cross-Attention Network (PV-Net) for 3D signature authentication in VR/AR, achieving a 2.5% Equal Error Rate on the DeepAirSig dataset and 76% accuracy on ImmAirsig, enhancing user-centric security without disrupting immersion.
Key Points
- PV-Net models local motion dynamics and global spatial structure from 3D trajectories.
- Evaluated on DeepAirSig with 1,800 signatures and ImmAirsig with 880 samples.
- Traditional authentication methods disrupt immersion and require external hardware.
- 3D behavioral interfaces offer seamless, natural interaction for user authentication.
- Potential applications in various immersive technology sectors.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2607. 01435v1 Announce Type: new 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.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
A phase-aware LLM agent optimizes human-object interaction retrieval, outperforming Optuna TPE by 33.3% and VDTuner by 34.2% on the HICO-DET benchmark. This method enhances throughput by 15.3x over UniIR and demonstrates strong transferability across vector database management systems.