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空中签名解锁:基于点-体素交叉注意力网络的虚拟与增强现实认证接口

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

Neda Abdolrahimi, Thiru Siddharth, Frank Sicong chen, Vir V Phoha

arXiv 2607.01435首次发表:更新:

AI 中文总结

提出一种空中签名认证接口,利用点-体素交叉注意力网络(PV-Net)建模3D轨迹的局部运动与全局结构,在公开和自建数据集上实现低错误率,支持沉浸式环境中的自然交互认证。

AI 中文摘要

虚拟现实和增强现实(VR/AR)等沉浸式技术的重大进步及其与现代生活各个方面的融合,需要安全、直观且与具身交互兼容的认证接口。传统方法(如密码、PIN码和基于设备的登录)会打破沉浸感并依赖外部硬件。最近的3D特定行为方法(如基于手势、眼动追踪和脑电图的方法)提供了有希望的替代方案,但通常需要专用传感器或限制自然运动,限制了在动态环境中的可用性。我们提出“空中签名解锁”,一种空中签名接口,使用户能够通过在3D空间中自然签名(一种熟悉、个性化且可重复的手势)进行认证。为实现该接口,我们设计了一个点-体素交叉注意力网络(PV-Net),从3D轨迹中联合建模局部运动动态和全局空间结构。该模型在两个数据集上进行了评估:公开的DeepAirSig数据集(40名用户的1800个签名)和ImmAirsig,一个使用Meta Quest 2在沉浸式VR中收集的新数据集(22名用户的880个样本)。PV-Net在DeepAirSig上实现了2.5%的等错误率,在ImmAirSig上实现了76%的分类准确率。这些发现凸显了3D行为接口在沉浸式环境中实现无缝、以用户为中心的认证(将安全性与自然交互融合)的潜力。

英文摘要

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.

CommentsChanged the 2nd co-author name from FRank Sicongchen to Frank Sicong chen

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