arXiv subjects

Pan Hui

Publications and source records attributed to Pan Hui.

At least 55 records · Page 3Linked to original sources

arXiv2025

Gauging the Competition: Understanding Social Comparison and Anxiety through Eye-tracking in Virtual Reality Group Interview

Virtual Reality (VR) is a promising tool for interview training, yet the psychological dynamics of group interviews, such as social comparison, remain underexplored. We investigate this phenomenon by developing an immersive VR group interview system and conducting an eye-tracking study with 73 participants. We manipulated peer performance using ambiguous behavioral cues (e.g., hand-raising) and objective information (public test scores) to measure their effect on participants' attention and self-concept. Our results demonstrate a "Big-Fish-Little-Pond Effect" in VR: an increase in high-achieving peer behaviors heightened participants' processing of social comparison information and significantly lowered their self-assessments. The introduction of objective scores further intensified these comparative behaviors. We also found that lower perceived realism of the VR environment correlated with higher anxiety. These findings offer key insights and design considerations for creating more effective and psychologically-aware virtual training environments that account for complex social dynamics.

cs.HC↗

arXiv2025

Generative Multi-Sensory Meditation: Exploring Immersive Depth and Activation in Virtual Reality

Mindfulness meditation has seen increasing applications in diverse domains as an effective practice to improve mental health. However, the standardized frameworks adopted by most applications often fail to cater to users with various psychological states and health conditions. This limitation arises primarily from the lack of personalization and adaptive content design. To address this, we propose MindfulVerse, an AI-Generated Content (AIGC)-driven application to create personalized and immersive mindfulness experiences. By developing a novel agent, the system can dynamically adjust the meditation content based on the ideas of individual users. Furthermore, we conducted exploratory user studies and comparative evaluations to assess the application scenarios and performance of our novel generative meditation tool in VR environments. The results of this user study indicate that generative meditation improves neural activation in self-regulation and shows a positive impact on emotional regulation and participation. Our approach offers a generative meditation procedure that provides users with an application that better suits their preferences and states.

cs.HC↗

arXiv2025

Unpacking Discourses on Childbirth and Parenthood in Popular Social Media Platforms Across China, Japan, and South Korea

Social media use has been shown to be associated with low fertility desires. However, we know little about the discourses surrounding childbirth and parenthood that people consume online. We analyze 219,127 comments on 668 short videos related to reproduction and parenthood from Douyin and Tiktok in China, South Korea, and Japan, a region famous for its extremely low fertility level, to examine the topics and sentiment expressed online. BERTopic model is used to assist thematic analysis, and a large language model QWen is applied to label sentiment. We find that comments focus on childrearing costs in all countries, utility of children, particularly in Japan and South Korea, and individualism, primarily in China. Comments from Douyin exhibit the strongest anti-natalist sentiments, while the Japanese and Korean comments are more neutral. Short video characteristics, such as their stances or account type, significantly influence the responses, alongside regional socioeconomic indicators, including GDP, urbanization, and population sex ratio. This work provides one of the first comprehensive analyses of online discourses on family formation via popular algorithm-fed video sharing platforms in regions experiencing low fertility rates, making a valuable contribution to our understanding of the spread of family values online.

cs.SI↗

arXiv2025

EyeNexus: Adaptive Gaze-Driven Quality and Bitrate Streaming for Seamless VR Cloud Gaming Experiences

Virtual Reality (VR) cloud gaming systems render the 3D graphics on cloud servers for playing graphically demanding games on VR headsets. Delivering high-resolution game scenes is challenging due to variation in network performance. By leveraging the non-uniform human vision perception, foveated rendering and encoding have proven effective for optimized streaming in constrained networks. SoTA foveation methods either do not incorporate real-time gaze data or are unable to handle variations in network conditions, resulting in a suboptimal user experience. We introduce EyeNexus, a pioneering system that combines real-time gaze-driven spatial compression (FSC) with gaze-driven video encoding (FVE), transforming the gaze point for precise alignment and foveation. We propose a novel foveation model that dynamically adjusts the foveation region based on real-time bandwidth and gaze data. The model simplifies network-aware quality assignment in FVE, ensuring smooth and imperceptible quality gradients. We evaluate EyeNexus using objective and subjective measures with different network conditions and games. EyeNexus reduces latency by up to 70.9% and improves perceptual visual quality by up to 24.6%. Our IRB-approved user study shows that EyeNexus achieves the highest playability and visual quality, with improvements of up to 48%, while eliminating motion sickness.

eess.IV↗

arXiv2025

Exploring Gaze Dynamics in VR Film Education: Gender, Avatar, and the Shift Between Male and Female Perspectives

In virtual reality (VR) education, especially in creative fields like film production, avatar design and narrative style extend beyond appearance and aesthetics. This study explores how the interaction between avatar gender, the dominant narrative actor's gender, and the learner's gender influences film production learning in VR, focusing on gaze dynamics and gender perspectives. Using a 2*2*2 experimental design, 48 participants operated avatars of different genders and interacted with male or female-dominant narratives. The results show that the consistency between the avatar and gender affects presence, and learners' control over the avatar is also influenced by gender matching. Learners using avatars of the opposite gender reported stronger control, suggesting gender incongruity prompted more focus on the avatar. Additionally, female participants with female avatars were more likely to adopt a "female gaze," favoring soft lighting and emotional shots, while male participants with male avatars were more likely to adopt a "male gaze," choosing dynamic shots and high contrast. When male participants used female avatars, they favored "female gaze," while female participants with male avatars focused on "male gaze". These findings advance our understanding of how avatar design and narrative style in VR-based education influence creativity and the cultivation of gender perspectives, and they offer insights for developing more inclusive and diverse VR teaching tools going forward.

cs.HC↗

arXiv2025

Talking Spell: A Wearable System Enabling Real-Time Anthropomorphic Voice Interaction with Everyday Objects

Virtual assistants (VAs) have become ubiquitous in daily life, integrated into smartphones and smart devices, sparking interest in AI companions that enhance user experiences and foster emotional connections. However, existing companions are often embedded in specific objects-such as glasses, home assistants, or dolls-requiring users to form emotional bonds with unfamiliar items, which can lead to reduced engagement and feelings of detachment. To address this, we introduce Talking Spell, a wearable system that empowers users to imbue any everyday object with speech and anthropomorphic personas through a user-centric radiative network. Leveraging advanced computer vision (e.g., YOLOv11 for object detection), large vision-language models (e.g., QWEN-VL for persona generation), speech-to-text and text-to-speech technologies, Talking Spell guides users through three stages of emotional connection: acquaintance, familiarization, and bonding. We validated our system through a user study involving 12 participants, utilizing Talking Spell to explore four interaction intentions: entertainment, companionship, utility, and creativity. The results demonstrate its effectiveness in fostering meaningful interactions and emotional significance with everyday objects. Our findings indicate that Talking Spell creates engaging and personalized experiences, as demonstrated through various devices, ranging from accessories to essential wearables.

cs.HC↗

arXiv2025

`My Dataset of Love': A Preliminary Mixed-Method Exploration of Human-AI Romantic Relationships

Human-AI romantic relationships have gained wide popularity among social media users in China. The technological impact on romantic relationships and its potential applications have long drawn research attention to topics such as relationship preservation and negativity mitigation. Media and communication studies also explore the practices in romantic para-social relationships. Nonetheless, this emerging human-AI romantic relationship, whether the relations fall into the category of para-social relationship together with its navigation pattern, remains unexplored, particularly in the context of relational stages and emotional attachment. This research thus seeks to fill this gap by presenting a mixed-method approach on 1,766 posts and 60,925 comments from Xiaohongshu, as well as the semi-structured interviews with 23 participants, of whom one of them developed her relationship with self-created AI for three years. The findings revealed that the users' willingness to self-disclose to AI companions led to increased positivity without social stigma. The results also unveiled the reciprocal nature of these interactions, the dominance of 'self', and raised concerns about language misuse, bias, and data security in AI communication.

cs.HC↗

arXiv2025

Simulating Generative Social Agents via Theory-Informed Workflow Design

Recent advances in large language models have demonstrated strong reasoning and role-playing capabilities, opening new opportunities for agent-based social simulations. However, most existing agents' implementations are scenario-tailored, without a unified framework to guide the design. This lack of a general social agent limits their ability to generalize across different social contexts and to produce consistent, realistic behaviors. To address this challenge, we propose a theory-informed framework that provides a systematic design process for LLM-based social agents. Our framework is grounded in principles from Social Cognition Theory and introduces three key modules: motivation, action planning, and learning. These modules jointly enable agents to reason about their goals, plan coherent actions, and adapt their behavior over time, leading to more flexible and contextually appropriate responses. Comprehensive experiments demonstrate that our theory-driven agents reproduce realistic human behavior patterns under complex conditions, achieving up to 75% lower deviation from real-world behavioral data across multiple fidelity metrics compared to classical generative baselines. Ablation studies further show that removing motivation, planning, or learning modules increases errors by 1.5 to 3.2 times, confirming their distinct and essential contributions to generating realistic and coherent social behaviors.

cs.AI↗

arXiv2025

EchoAid: Enhancing Livestream Shopping Accessibility for the DHH Community

Livestream shopping platforms often overlook the accessibility needs of the Deaf and Hard of Hearing (DHH) community, leading to barriers such as information inaccessibility and overload. To tackle these challenges, we developed \textit{EchoAid}, a mobile app designed to improve the livestream shopping experience for DHH users. \textit{EchoAid} utilizes advanced speech-to-text conversion, Rapid Serial Visual Presentation (RSVP) technology, and Large Language Models (LLMs) to simplify the complex information flow in live sales environments. We conducted exploratory studies with eight DHH individuals to identify design needs and iteratively developed the \textit{EchoAid} prototype based on feedback from three participants. We then evaluate the performance of this system in a user study workshop involving 38 DHH participants. Our findings demonstrate the successful design and validation process of \textit{EchoAid}, highlighting its potential to enhance product information extraction, leading to reduced cognitive overload and more engaging and customized shopping experiences for DHH users.

cs.HC↗

arXiv2025

Metabook: A Mobile-to-Headset Pipeline for 3D Story Book Creation in Augmented Reality

The AR 3D book has shown significant potential in enhancing students' learning outcomes. However, the creation process of 3D books requires a significant investment of time, effort, and specialized skills. Thus, in this paper, we first conduct a three-day workshop investigating how AI can support the automated creation of 3D books. Informed by the design insights derived from the workshop, we developed Metabook, a system that enables even novice users to create 3D books from text automatically. To our knowledge, Metabook is the first system to offer end-to-end 3D book generation. A follow-up study with adult users indicates that Metabook enables inexperienced users to create 3D books, achieving reduced efforts and shortened preparation time. We subsequently recruited 22 children to examine the effects of AR 3D books on children's learning compared with paper-based books. The findings indicate that 3D books significantly enhance children's interest, improve memory retention, and reduce cognitive load, though no significant improvement was observed in comprehension. We conclude by discussing strategies for more effectively leveraging 3D books to support children's learning and offer practical recommendations for educators.

cs.HC↗

arXiv2025

CineVision: An Interactive Pre-visualization Storyboard System for Director-Cinematographer Collaboration

Effective communication between directors and cinematographers is fundamental in film production, yet traditional approaches relying on visual references and hand-drawn storyboards often lack the efficiency and precision necessary during pre-production. We present CineVision, an AI-driven platform that integrates scriptwriting with real-time visual pre-visualization to bridge this communication gap. By offering dynamic lighting control, style emulation based on renowned filmmakers, and customizable character design, CineVision enables directors to convey their creative vision with heightened clarity and rapidly iterate on scene composition. In a 24-participant lab study, CineVision yielded shorter task times and higher usability ratings than two baseline methods, suggesting a potential to ease early-stage communication and accelerate storyboard drafts under controlled conditions. These findings underscore CineVision's potential to streamline pre-production processes and foster deeper creative synergy among filmmaking teams, particularly for new collaborators. Our code and demo are available at https://github.com/TonyHongtaoWu/CineVision.

cs.HC↗

arXiv2025

DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation

Generating 3D scenes from natural language holds great promise for applications in gaming, film, and design. However, existing methods struggle with automation, 3D consistency, and fine-grained control. We present DreamScene, an end-to-end framework for high-quality and editable 3D scene generation from text or dialogue. DreamScene begins with a scene planning module, where a GPT-4 agent infers object semantics and spatial constraints to construct a hybrid graph. A graph-based placement algorithm then produces a structured, collision-free layout. Based on this layout, Formation Pattern Sampling (FPS) generates object geometry using multi-timestep sampling and reconstructive optimization, enabling fast and realistic synthesis. To ensure global consistent, DreamScene employs a progressive camera sampling strategy tailored to both indoor and outdoor settings. Finally, the system supports fine-grained scene editing, including object movement, appearance changes, and 4D dynamic motion. Experiments demonstrate that DreamScene surpasses prior methods in quality, consistency, and flexibility, offering a practical solution for open-domain 3D content creation. Code and demos are available at https://jahnsonblack.github.io/DreamScene-Full/.

cs.CV↗

arXiv2025

Rejection or Inclusion in the Emotion-Identity Dynamics of TikTok Refugees on RedNote

This study examines cross-cultural interactions between Chinese users and self-identified "TikTok Refugees"(foreign users who migrated to RedNote after TikTok's U.S. ban). Based on a dataset of 1,862 posts and 403,054 comments, we use large language model-based sentiment classification and BERT-based topic modelling to explore how both groups engage with the TikTok refugee phenomenon. We analyse what themes foreign users express, how Chinese users respond, how stances (Pro-China, Neutral, Pro-Foreign) shape emotional expression, and how affective responses differ across topics and identities. Results show strong affective asymmetry: Chinese users respond with varying emotional intensities across topics and stances: pride and praise dominate cultural threads, while political discussions elicit high levels of contempt and anger, especially from Pro-China commenters. Pro-Foreign users exhibit the strongest negative emotions across all topics, whereas neutral users express curiosity and joy but still reinforce mainstream discursive norms. Cross-topic comparisons reveal that appearance-related content produces the most emotionally balanced interactions, while politics generates the highest polarization. Our findings reveal distinct emotion-stance structures in Sino-foreign online interactions and offer empirical insights into identity negotiation in transnational digital publics.

cs.SI↗

arXiv2025

Pinning "Reflection" on the Agenda: Investigating Reflection in Human-LLM Co-Creation for Creative Coding

Large language models (LLMs) are increasingly integrated into creative coding, yet how users reflect, and how different co-creation conditions influence reflective behavior, remains underexplored. This study investigates situated, moment-to-moment reflection in creative coding under two prompting strategies: the entire task invocation (T1) and decomposed subtask invocation (T2), to examine their effects on reflective behavior. Our mixed-method results reveal three distinct reflection types and show that T2 encourages more frequent, strategic, and generative reflection, fostering diagnostic reasoning and goal redefinition. These findings offer insights into how LLM-based tools foster deeper creative engagement through structured, behaviorally grounded reflection support.

cs.HC↗

arXiv2025

When Trust Collides: Decoding Human-LLM Cooperation Dynamics through the Prisoner's Dilemma

As large language models (LLMs) become increasingly capable of autonomous decision-making, they introduce new challenges and opportunities for human-AI cooperation in mixed-motive contexts. While prior research has primarily examined AI in assistive or cooperative roles, little is known about how humans interact with AI agents perceived as independent and strategic actors. This study investigates human cooperative attitudes and behaviors toward LLM agents by engaging 30 participants (15 males, 15 females) in repeated Prisoner's Dilemma games with agents differing in declared identity: purported human, rule-based AI, and LLM agent. Behavioral metrics, including cooperation rate, decision latency, unsolicited cooperative acts and trust restoration tolerance, were analyzed to assess the influence of agent identity and participant gender. Results revealed significant effects of declared agent identity on most cooperation-related behaviors, along with notable gender differences in decision latency. Furthermore, qualitative responses suggest that these behavioral differences were shaped by participants interpretations and expectations of the agents. These findings contribute to our understanding of human adaptation in competitive cooperation with autonomous agents and underscore the importance of agent framing in shaping effective and ethical human-AI interaction.

cs.HC↗

arXiv2025

Framing metaverse identity: A multidimensional framework for governing digital selves

This paper proposes a multidimensional framework for Metaverse Identity, addressing its definition, guiding principles, and critical challenges. Metaverse Identity is conceptualized as a users digital self, encompassing personal attributes, data footprints, social roles, and economic elements. To elucidate its core characteristics and implications, this framework introduces two guiding principles: Equivalence and Alignment, and Fusion and Expansiveness. The first principle advocates for consistency between metaverse and real-world identities in behavioral norms and social standards, ensuring rights protection and establishing conduct guidelines. The second emphasizes the deep integration and transformative evolution of metaverse identities, enabling them to transcend real-world constraints, meet diverse needs, and foster inclusivity. Together, these principles serve as complementary pillars, balancing ethical integration with dynamic co-evolution. Building on this foundation, the study identifies five critical challenges: interoperability, legal boundaries, privacy and identity management, risks from deepfakes and synthetic identities, and identity fragmentation impacting psychological well-being. To address these challenges, strategic recommendations are offered to guide stakeholders. By constructing this framework, the study fills a key theoretical gap, advances systematic research, and provides a foundation for policies and governance strategies to address the complexities of metaverse identities in a rapidly evolving digital domain.

cs.CY↗

arXiv2025

M3G: Multi-Granular Gesture Generator for Audio-Driven Full-Body Human Motion Synthesis

Generating full-body human gestures encompassing face, body, hands, and global movements from audio is a valuable yet challenging task in virtual avatar creation. Previous systems focused on tokenizing the human gestures framewisely and predicting the tokens of each frame from the input audio. However, one observation is that the number of frames required for a complete expressive human gesture, defined as granularity, varies among different human gesture patterns. Existing systems fail to model these gesture patterns due to the fixed granularity of their gesture tokens. To solve this problem, we propose a novel framework named Multi-Granular Gesture Generator (M3G) for audio-driven holistic gesture generation. In M3G, we propose a novel Multi-Granular VQ-VAE (MGVQ-VAE) to tokenize motion patterns and reconstruct motion sequences from different temporal granularities. Subsequently, we proposed a multi-granular token predictor that extracts multi-granular information from audio and predicts the corresponding motion tokens. Then M3G reconstructs the human gestures from the predicted tokens using the MGVQ-VAE. Both objective and subjective experiments demonstrate that our proposed M3G framework outperforms the state-of-the-art methods in terms of generating natural and expressive full-body human gestures.

cs.GR↗

arXiv2025

A Comprehensive Survey of Electrical Stimulation Haptic Feedback in Human-Computer Interaction

Haptic perception and feedback play a pivotal role in interactive experiences, forming an essential component of human-computer interaction (HCI). In recent years, the field of haptic interaction has witnessed significant advancements, particularly in the area of electrical haptic feedback, driving innovation across various domains. To gain a comprehensive understanding of the current state of research and the latest developments in electrical haptic interaction, this study systematically reviews the literature in this area. Our investigation covers key aspects including haptic devices, haptic perception mechanisms, the comparison and integration of electrical haptic feedback with other feedback modalities, and their diverse applications. Specifically, we conduct a systematic analysis of 110 research papers to explore the forefront of electrical haptic feedback, providing insights into its latest trends, challenges, and future directions.

cs.HC↗