arXiv subjects

Tangyao Li

Publications and source records attributed to Tangyao Li.

5 recordsLinked to original sources

arXiv2025

From Adaptation to Intelligence: A Systematic Review of Data, Strategies, and Impact in Personalized VR

As virtual reality (VR) systems advance, they are increasingly expected to adapt intelligently to individual users' states, abilities, and preferences. While prior research has examined user-state sensing and adaptive interaction design in VR, existing reviews typically address these aspects in isolation. In this paper, we examine the growing body of research on personalization in VR, with a particular focus on how user data collected during immersion is used to drive adaptive strategies that tailor the experience and enhance engagement, performance, or other specific goals. We synthesize findings from studies that employ adaptive techniques across diverse application domains and summarize a five-stage conceptual framework that unifies adaptive mechanisms across domains. Our analysis reveals emerging trends, including the integration of multimodal sensors, the transition from purely reactive to hybrid adaptation systems, and the adoption of artificial intelligence approaches. Finally, we identify key challenges related to data, modeling, and evaluation, and outline future research directions toward more effective and user-centered VR systems.

cs.HC↗

arXiv2025

A comparative study of sensory encoding models for human navigation in virtual reality

In virtual reality applications, users often navigate through virtual environments, but the issue of physiological responses, such as cybersickness, fatigue, and cognitive workload, can disrupt or even halt these activities. Despite its impact, the underlying mechanisms of how the sensory system encodes information in VR remain unclear. In this study, we compare three sensory encoding models, Bayesian Efficient Coding, Fitness Maximizing Coding, and the Linear Nonlinear Poisson model, regarding their ability to simulate human navigation behavior in VR. By incorporating the factor of physiological responses into the models, we find that the Bayesian Efficient Coding model generally outperforms the others. Furthermore, the Fitness Maximizing Code framework provides more accurate estimates when the error penalty is small. Our results suggest that the Bayesian Efficient Coding framework offers superior predictions in most scenarios, providing a better understanding of human navigation behavior in VR environments.

cs.HC↗

arXiv2025

Balancing Exploration and Cybersickness: Investigating Curiosity-Driven Behavior in Virtual Environments

During virtual navigation, users exhibit varied interaction and navigation behaviors influenced by several factors. Existing theories and models have been developed to explain and predict these diverse patterns. While users often experience uncomfortable sensations, such as cybersickness, during virtual reality (VR) use, they do not always make optimal decisions to mitigate these effects. Although methods like reinforcement learning have been used to model decision-making processes, they typically rely on random selection to simulate actions, failing to capture the complexities of real navigation behavior. In this study, we propose curiosity as a key factor driving irrational decision-making, suggesting that users continuously balance exploration and cybersickness according to the free energy principle during virtual navigation. Our findings show that VR users generally adopt conservative strategies when navigating, with most participants displaying negative curiosity across trials. However, curiosity levels tend to rise when the virtual environment changes, illustrating the dynamic interplay between exploration and discomfort. This study provides a quantitative approach to decoding curiosity-driven behavior during virtual navigation, offering insights into how users balance exploration and the avoidance of cybersickness. Future research will further refine this model by incorporating additional psychological and environmental factors to improve the accuracy of navigation pattern predictions.

cs.HC↗

arXiv2025

Towards Consumer-Grade Cybersickness Prediction: Multi-Model Alignment for Real-Time Vision-Only Inference

Cybersickness remains a major obstacle to the widespread adoption of immersive virtual reality (VR), particularly in consumer-grade environments. While prior methods rely on invasive signals such as electroencephalography (EEG) for high predictive accuracy, these approaches require specialized hardware and are impractical for real-world applications. In this work, we propose a scalable, deployable framework for personalized cybersickness prediction leveraging only non-invasive signals readily available from commercial VR headsets, including head motion, eye tracking, and physiological responses. Our model employs a modality-specific graph neural network enhanced with a Difference Attention Module to extract temporal-spatial embeddings capturing dynamic changes across modalities. A cross-modal alignment module jointly trains the video encoder to learn personalized traits by aligning video features with sensor-derived representations. Consequently, the model accurately predicts individual cybersickness using only video input during inference. Experimental results show our model achieves 88.4\% accuracy, closely matching EEG-based approaches (89.16\%), while reducing deployment complexity. With an average inference latency of 90ms, our framework supports real-time applications, ideal for integration into consumer-grade VR platforms without compromising personalization or performance. The code will be relesed at https://github.com/U235-Aurora/PTGNN.

cs.CV↗

arXiv2021

When Creators Meet the Metaverse: A Survey on Computational Arts

The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g., virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reflected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs. Finally, we propose several research agendas: democratising computational arts, digital privacy, and safety for metaverse artists, ownership recognition for digital artworks, technological challenges, and so on. The survey also serves as introductory material for artists and metaverse technologists to begin creations in the realm of surrealistic cyberspace.

cs.CY↗