arXiv subjects

Joseph J. LaViola Jr

Publications and source records attributed to Joseph J. LaViola Jr.

11 recordsLinked to original sources

arXiv2026

Modernizing User Privacy Preference Measurement through GPPI: A GDPR-aligned Privacy Preference Item Bank

Privacy measurement instruments (e.g., CFIP, IUIPC, PAQ) predate GDPR by over a decade and measure privacy concerns, distinct from preferences for regulatory protections (e.g., data portability, erasure, automated decision-making rights). This leaves practitioners without tools to assess whether users value the GDPR mechanisms implemented in compliant policies. We developed a GDPR-grounded privacy preference measurement item bank by extracting 669 statements from all 99 GDPR articles, validated by: (1) two-round expert review achieving full consensus on accuracy, (2) semantic clustering into 10 parent themes and 87 subthemes, and (3) consensus review with 50 privacy experts (5 per theme) using a larger or equal than 4/5 vote retention threshold. The final 527-item bank comprises 9 parent themes and 73 subthemes (18 to 112 items per parent theme, 1 to 29 per subtheme), enabling targeted measurement across granularities while covering GDPR at mean pairwise expert agreement of approx. 85%. This work introduces a complementary measurement dimension aligning user preferences with regulatory mechanisms.

cs.HC↗

arXiv2026

From One World to Another: Interfaces for Efficiently Transitioning Between Virtual Environments

Personal computers and handheld devices provide keyboard shortcuts and swipe gestures to enable users to efficiently switch between applications, whereas today's virtual reality (VR) systems do not. In this work, we present an exploratory study on user interface aspects to support efficient switching between worlds in VR. We created eight interfaces that afford previewing and selecting from the available virtual worlds, including methods using portals and worlds-in-miniature (WiMs). To evaluate these methods, we conducted a controlled within-subjects empirical experiment (N=22) where participants frequently transitioned between six different environments to complete an object collection task. Our quantitative and qualitative results show that WiMs supported rapid acquisition of high-level spatial information while searching and were deemed most efficient by participants while portals provided fast pre-orientation. Finally, we present insights into the applicability, usability, and effectiveness of the VR world switching methods we explored, and provide recommendations for their application and future context/world switching techniques and interfaces.

cs.HC↗

arXiv2025

Cam-2-Cam: Exploring the Design Space of Dual-Camera Interactions for Smartphone-based Augmented Reality

Off-the-shelf smartphone-based AR systems typically use a single front-facing or rear-facing camera, which restricts user interactions to a narrow field of view and small screen size, thus reducing their practicality. We present Cam-2-Cam, an interaction concept implemented in three smartphone-based AR applications with interactions that span both cameras. Results from our qualitative analysis conducted on 30 participants presented two major design lessons that explore the interaction space of smartphone AR while maintaining critical AR interface attributes like embodiment and immersion: (1) Balancing Contextual Relevance and Feedback Quality serves to outline a delicate balance between implementing familiar interactions people do in the real world and the quality of multimodal AR responses and (2) Preventing Disorientation using Simultaneous Capture and Alternating Cameras which details how to prevent disorientation during AR interactions using the two distinct camera techniques we implemented in the paper. Additionally, we consider observed user assumptions or natural tendencies to inform future implementations of dual-camera setups for smartphone-based AR. We envision our design lessons as an initial pioneering step toward expanding the interaction space of smartphone-based AR, potentially driving broader adoption and overcoming limitations of single-camera AR.

cs.HC↗

arXiv2025

Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks

We explore natural user interactions using a virtual reality simulation of a robot arm for assembly tasks. Using a Wizard-of-Oz study, participants completed collaborative LEGO and instructive PCB assembly tasks, with the robot responding under experimenter control. We collected voice, hand tracking, and gaze data from users. Statistical analyses revealed that instructive and collaborative scenarios elicit distinct behaviors and adopted strategies, particularly as tasks progress. Users tended to use put-that-there language in spatially ambiguous contexts and more descriptive instructions in spatially clear ones. Our contributions include the identification of natural interaction strategies through analyses of collected data, as well as the supporting dataset, to guide the understanding and design of natural multimodal user interfaces for instructive interaction with systems in virtual reality.

cs.HC↗

arXiv2025

Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents

We investigated the challenges of mitigating response delays in free-form conversations with virtual agents powered by Large Language Models (LLMs) within Virtual Reality (VR). For this, we used conversational fillers, such as gestures and verbal cues, to bridge delays between user input and system responses and evaluate their effectiveness across various latency levels and interaction scenarios. We found that latency above 4 seconds degrades quality of experience, while natural conversational fillers improve perceived response time, especially in high-delay conditions. Our findings provide insights for practitioners and researchers to optimize user engagement whenever conversational systems' responses are delayed by network limitations or slow hardware. We also contribute an open-source pipeline that streamlines deploying conversational agents in virtual environments.

cs.HC↗

arXiv2025

Exploring Perception-Based Techniques for Redirected Walking in VR: A Comprehensive Survey

We present a comprehensive survey of perception-based redirected walking (RDW) techniques in virtual reality (VR), presenting a taxonomy that serves as a framework for understanding and designing RDW algorithms. RDW enables users to explore virtual environments (VEs) larger than their physical space, addressing the constraints of real walking in limited home VR setups. Our review spans 232 papers, with 165 included in the final analysis. We categorize perception-based RDW techniques based on gains, gain application, target orientation calculation, and optional general enhancements, identifying key patterns and relationships. We present data on how current work aligns within this classification system and suggest how this data can guide future work into areas that are relatively under explored. This taxonomy clarifies perception-based RDW techniques, guiding the design and application of RDW systems, and suggests future research directions to enhance VR user experience.

cs.HC↗

arXiv2025

Takeaways from Applying LLM Capabilities to Multiple Conversational Avatars in a VR Pilot Study

We present a virtual reality (VR) environment featuring conversational avatars powered by a locally-deployed LLM, integrated with automatic speech recognition (ASR), text-to-speech (TTS), and lip-syncing. Through a pilot study, we explored the effects of three types of avatar status indicators during response generation. Our findings reveal design considerations for improving responsiveness and realism in LLM-driven conversational systems. We also detail two system architectures: one using an LLM-based state machine to control avatar behavior and another integrating retrieval-augmented generation (RAG) for context-grounded responses. Together, these contributions offer practical insights to guide future work in developing task-oriented conversational AI in VR environments.

cs.HC↗

arXiv2023

Effects of Clutter on Egocentric Distance Perception in Virtual Reality

To assess the impact of clutter on egocentric distance perception, we performed a mixed-design study with 60 participants in four different virtual environments (VEs) with three levels of clutter. Additionally, we compared the indoor/outdoor VE characteristics and the HMD's FOV. The participants wore a backpack computer and a wide FOV head-mounted display (HMD) as they blind-walked towards three distinct targets at distances of 3m, 4.5m, and 6m. The HMD's field of view (FOV) was programmatically limited to 165°$\times$110°, 110°$\times$110°, or 45°$\times$35°. The results showed that increased clutter in the environment led to more precise distance judgment and less underestimation, independent of the FOV. In comparison to outdoor VEs, indoor VEs showed more accurate distance judgment. Additionally, participants made more accurate judgements while looking at the VEs through wider FOVs.

cs.HC↗

arXiv2021

SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild

Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more. Recognition of hand gestures can be nowadays performed directly from the stream of hand skeletons estimated by software provided by low-cost trackers (Ultraleap) and MR headsets (Hololens, Oculus Quest) or by video processing software modules (e.g. Google Mediapipe). Despite the recent advancements in gesture and action recognition from skeletons, it is unclear how well the current state-of-the-art techniques can perform in a real-world scenario for the recognition of a wide set of heterogeneous gestures, as many benchmarks do not test online recognition and use limited dictionaries. This motivated the proposal of the SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild. For this contest, we created a novel dataset with heterogeneous gestures featuring different types and duration. These gestures have to be found inside sequences in an online recognition scenario. This paper presents the result of the contest, showing the performances of the techniques proposed by four research groups on the challenging task compared with a simple baseline method.

cs.CV↗

arXiv2020

DeepNAG: Deep Non-Adversarial Gesture Generation

Synthetic data generation to improve classification performance (data augmentation) is a well-studied problem. Recently, generative adversarial networks (GAN) have shown superior image data augmentation performance, but their suitability in gesture synthesis has received inadequate attention. Further, GANs prohibitively require simultaneous generator and discriminator network training. We tackle both issues in this work. We first discuss a novel, device-agnostic GAN model for gesture synthesis called DeepGAN. Thereafter, we formulate DeepNAG by introducing a new differentiable loss function based on dynamic time warping and the average Hausdorff distance, which allows us to train DeepGAN's generator without requiring a discriminator. Through evaluations, we compare the utility of DeepGAN and DeepNAG against two alternative techniques for training five recognizers using data augmentation over six datasets. We further investigate the perceived quality of synthesized samples via an Amazon Mechanical Turk user study based on the HYPE benchmark. We find that DeepNAG outperforms DeepGAN in accuracy, training time (up to 17x faster), and realism, thereby opening the door to a new line of research in generator network design and training for gesture synthesis. Our source code is available at https://www.deepnag.com.

cs.CV↗

arXiv2019

DeepGRU: Deep Gesture Recognition Utility

We propose DeepGRU, a novel end-to-end deep network model informed by recent developments in deep learning for gesture and action recognition, that is streamlined and device-agnostic. DeepGRU, which uses only raw skeleton, pose or vector data is quickly understood, implemented, and trained, and yet achieves state-of-the-art results on challenging datasets. At the heart of our method lies a set of stacked gated recurrent units (GRU), two fully-connected layers and a novel global attention model. We evaluate our method on seven publicly available datasets, containing various number of samples and spanning over a broad range of interactions (full-body, multi-actor, hand gestures, etc.). In all but one case we outperform the state-of-the-art pose-based methods. For instance, we achieve a recognition accuracy of 84.9% and 92.3% on cross-subject and cross-view tests of the NTU RGB+D dataset respectively, and also 100% recognition accuracy on the UT-Kinect dataset. While DeepGRU works well on large datasets with many training samples, we show that even in the absence of a large number of training data, and with as little as four samples per class, DeepGRU can beat traditional methods specifically designed for small training sets. Lastly, we demonstrate that even without powerful hardware, and using only the CPU, our method can still be trained in under 10 minutes on small-scale datasets, making it an enticing choice for rapid application prototyping and development.

cs.CV↗