Search Results (473)

This paper proposes a unified methodological framework for evaluating heterogeneous approaches to avatar-based sign language visualization. The study introduces a four-dimensional analytical framework based on four independent criteria: (A1) pipeline architecture and degree of automation, (A2) data and annotation requirements, (A3) portability across sign languages and domains, and (A4) integration and accessibility. The framework is applied to a comparative analysis of three dominant paradigms: (P1) notation → animation (e.g., HamNoSys), (P2) writing-based representation → animation (e.g., SignWriting), and (P3) keypoint-based animation and Artificial Intelligence (AI) methods. The comparative assessment shows that the differences between the paradigms are structural and reflect trade-offs among linguistic accuracy, automation level, scalability, and user accessibility, rather than the superiority of any one technology. Overall, the structured comparative framework (A1–A4) is applied for analyzing three paradigms of sign language avatar generation. It enables a systematic evaluation of architectural, data-related, and practical characteristics, highlighting key trade-offs between linguistic accuracy, scalability, and accessibility. Full article

Taking J.R.R. Tolkien’s portrayal of mercy in The Lord of the Rings as a point of departure, this article examines a question long debated in Dharmic commentarial traditions: what are the roles of individual effort and grace in completing the path to God-realization? The Indian spiritual teacher Meher Baba (1894–1969) offers a cosmology in which consciousness evolves by winding impressions (saṃskāras) through millions of lifetimes and progresses by unwinding them in thousands more, yet cannot complete this unwinding through effort alone. The final wiping out of all impressions requires the grace of a Sadguru or God-realized “Perfect Master.” This necessity is structural on two grounds, both rooted in the nature of consciousness itself. Building on Murshida Carol Weyland Conner’s distinction between “ascendant” and “descendant” paths of God-realization, this article examines what Meher Baba claimed to accomplish as Avatar: the cutting of a hole through which unprecedented divine light descends into physical creation. The descendant epoch inaugurated by this work shifts the orientation of incarnate existence from liberation out of matter toward progressive perfection within it. The wheel of rebirth is not abolished in this view. Through the Avatar’s wounded body, it is wounded into a new form, its substrate becoming divinized matter and its telos becoming perfection. Grace operates not only at the threshold of individual liberation but throughout the field of reincarnation itself. Full article

This study addresses the phenomenon of the hikikomori syndrome and escapism into digital environments. We examined the associations between digital escapism and identified supportive factors contributing to the liminal state between the real and digital worlds among digital hikikomori individuals. The case study captures, through in-depth interviews, the life situations of five hikikomori individuals aged 27–33 from selected countries: France, Russia, North America, Malaysia and Japan. The study covers the period from June 2025 to January 2026. Escapism into the digital environment is associated with the consumption of narrative digital content and digital games. Characters and avatars play a significant role in escapism. By identifying with characters and avatars, digital hikikomori reflect on their own life stories, exercise emotional self-regulation, and control their digital experience in a safe environment. Stressful life situations are the driving force behind the creation of a virtual identity. Through characters and avatars, digital hikikomori not only engage in self-reflection but also present their own identities, abilities, character traits, and personalities absent in the real world. They likewise substitute psychological and relational needs. Escapism into the digital environment, time investment in consuming narrative digital content, building a virtual identity, and progress in the digital environment that saturates self-assertion in the real environment are, in relation to the real environment, prerequisites for stagnation, procrastination, and liminal experience. Full article

Speech-driven 3D cartoon facial animation remains underexplored due to the difficulty of handling heterogeneous geometries with exaggerated proportions, limited generalization to diverse unseen subjects, and the scarcity of datasets. To address these challenges, we propose Let Toon Talk, a two-stage cascaded framework that effectively mitigates these bottlenecks in both modeling and driving. It enables one-shot, speech-synchronized 3D animation from a single unseen humanoid cartoon image, driven by arbitrary audio. Specifically, for avatar modeling, we propose a parametric adaptation mechanism to capture diverse heterogeneous facial topologies, which subsequently guides a feed-forward reconstruction module to create high-quality 3D Gaussian Splatting (3DGS) avatars. Building upon this, for speech driving, we introduce an Identity-Adaptive Flow Matching network. This generative module effectively maps audio to precise facial dynamics, achieving identity-adaptive motion synthesis for diverse humanoid cartoon characters without per-subject pretraining. Furthermore, we construct a hybrid cartoon talking-face dataset with a systematic curation strategy to bridge the data gap. Extensive experiments demonstrate that our framework produces high-quality, temporally coherent animations, exhibiting effective generalization on unseen structurally humanoid cartoon characters. Full article

Current research directed toward exploring the complexities of experiences within video gaming culture often comprises male-majority yet mixed-gender samples. Although valuable, these findings do not provide a male-representative overview of male gamers and risk diluting male gamer experiences as universal to all gamers, losing valuable gendered perspectives. In our study, we aimed to bridge this research gap by addressing: “What are the experiences of male gamers in online video gaming environments?” Through a qualitative, exploratory approach, underpinned by social constructionist epistemology, we conducted semi-structured interviews with 12 Australian adult male-identifying people who self-identified as online gamers (aged 18–36 years). Interviews were analysed through Reflexive Thematic Analysis, and findings present an overview of the complex social dynamics that shape male gamer experiences. Participants discussed experiences with toxicity online and frequently attributed problematic behaviour to characteristics they described as unrepresentative of male gamers broadly. They further described the sophisticated nature of online socialisation regarding the depth of bonds formed through gaming, which, at times, constitute larger online communities. These were navigated through a multitude of social criteria, revealing the underlying sociological structures that maintain dynamics within gaming environments. As such, broader concerns for the sociocultural status of men arose, particularly the problematisation of masculinity, which participants countered through identity management strategies aimed at restoring their reputation. Our findings highlight implications surrounding the importance of accounting for gendered meaning within gaming-based academic discourse and encourage public discourse surrounding problematic behaviour online to be redirected toward systems-level approaches. Full article

Digital mental health interventions (DMHIs) can help bridge treatment gaps experienced by young adults with co-occurring substance misuse and depression. However, it remains unclear whether engagement with these interventions differs for young adults with co-occurring conditions compared to those experiencing substance misuse or depression alone. To investigate this issue, we assessed working alliance and subjective engagement with a digital avatar-assisted cognitive-behavioral therapy (CBT) treatment platform (RITch^®CBT), comparing young adults with substance use, depression, and the co-occurrence of the two. A secondary data analysis was conducted on a sample of 99 young adults aged 18–28 years who presented at an urban university clinic. Participants rated their alliance and engagement following two brief sessions of the RITch^®CBT platform. Participants were then categorized into behavioral health groups. Repeated exposure to the program had a greater impact on subjective engagement and usability across diagnostic conditions, but there was no difference in working alliance reported across sessions or behavioral health groups. Further, participants’ depressive symptoms were significantly correlated with the number of sessions they expressed they were willing to engage in and attend. Our findings suggest that digital tools may support early engagement in treatment for young adults, regardless of presenting problem. Full article

Psychosis presents significant treatment challenges, and standard Cognitive Behavioral Therapy for psychosis often faces limitations due to patient engagement issues and reliance on subjective self-reporting. The integration of Virtual Reality (VR), physiological biosensors, and artificial intelligence offers a transformative opportunity to address these challenges. A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. A thorough literature search was performed across seven databases. Twelve randomized controlled trials involving 1504 participants were included to assess VR-assisted CBT, VR treatment, and AVATAR therapy. Meta-analyses showed that VR interventions significantly decreased auditory verbal hallucinations (pooled SMD = −0.24, p = 0.0011) and paranoid thoughts (SMD = −0.26, p < 0.0001) compared to control conditions. This review supports integrating multi-modal biosensors to collect real-time, objective physiological data. Such integration enables the development of AI-driven, closed-loop systems that dynamically adjust the virtual environment based on the patient’s physiological state. VR-assisted therapies effectively reduce positive symptoms of psychosis. Incorporating biosensors is a crucial step toward a data-driven approach for personalized, closed-loop psychiatric care. Future efforts should focus on large-scale clinical trials, biomarker validation, and robust ethical frameworks to ensure safe and effective implementation. Full article

Across modern Asia and many other regions, artificial intelligence is transforming religious life in diverse and profound ways. Robot priests chant sutras at Japanese Buddhist temples, AI-powered apps offer personalised coaching in Quranic recitation to millions of Muslims, and bereaved families consult algorithm-generated avatars of the deceased in China. They are neither merely tools for instrumental use nor channels for transmitting pre-existing religious authority. Instead, they create new forms of religious content, new types of spiritual encounters for religious users, and new structures of authority. This paper argues that understanding these phenomena requires theoretical innovation beyond simply applying existing concepts to new domains. Drawing on Actor–Network Theory, algorithmic culture studies, and scholarship on Asian religious traditions, the paper proposes the theoretical framework of generative charisma, theorising how AI systems gain religious authority through three interconnected mechanisms: captivation by generation, intimacy trust through personalisation, and oscillating enchantment. It also highlights accountability as a structural issue that needs critical discussion regarding governance. The paper demonstrates the framework’s usefulness by examining AI recitation coaching in Islamic practice and AI grief avatars in Chinese Buddhist mourning, showing its relevance across different religious traditions and technological forms. Full article

This study examines how immersive narrative resources, whether technological–sensory, narrative–structural, or contextual, are deployed in contemporary blockbuster cinema and to what extent audiences recognize and value them in their evaluations. Using Avatar: Fire and Ash as a case study, the research follows a sequential mixed-methods design. In the first phase, a qualitative film analysis identifies eight types of cognitive immersion, drawing on established theoretical frameworks of narrative immersion. The second phase is quantitative and involves the computational analysis of 1133 valid reviews from Internet Movie Database (IMDb) through Natural Language Processing (NLP) techniques, including n-gram frequency analysis, Latent Dirichlet Allocation (LDA) topic modeling with 3 topics after perplexity minimization, and sentiment polarity analysis. The LDA model reveals three discursive clusters, experiential and emotional, technical and comparative, and critical, with the latter concentrated mostly in low-rated reviews. Text sentiment and numeric ratings show a moderate positive correlation (r = 0.53, p < 0.001), pointing to a general but imperfect alignment between the two modes of evaluation. Markers of content fatigue (nothing new, predictable, boring) appear in 25.1% of the reviews, yet a third of those are still rated 8 or higher. When cross-tabulating the immersion categories with audience language, phenomenological and affective dimensions such as Emotional Engagement (59.8%) and Haptic/Sensory Experience (59.1%) emerge as the most frequently discussed, while cinematographic techniques like Bracketing (2.6%) are barely mentioned. Taken together, the findings suggest that the franchise sustains its appeal through a form of embodied sensory engagement that operates largely independent of narrative novelty. Full article

This study focuses on the transfer of the celebrity effect to live-stream e-commerce. It examines how the effectiveness of persuasion and the underlying mechanisms change when celebrities shift from live human appearances to AI avatars. Integrating Uncanny Valley Theory and Source Credibility Theory, and conducting a PLS-SEM analysis on 391 valid questionnaires collected from October to November 2025, reveals that, compared to live streaming by real celebrities, virtual streamers using celebrity avatars trigger significantly higher levels of perceived eeriness among consumers. This perceived eeriness systematically weakens audience evaluations of the streamer’s credibility, attractiveness, and expertise, ultimately leading to a decline in purchase intention. The findings suggest that, when the celebrity effect relies on an AI avatar, the persuasive pathway is negatively moderated by technological mediation. Among the dimensions of source credibility, trustworthiness is most directly eroded, while expertise remains the core factor driving purchase decisions. From a human-versus-avatar perspective, this study reveals the key psychological mechanisms underlying the digital migration of the celebrity effect. The results have important theoretical implications for understanding the boundaries of source credibility in digital communication and offer practical insights into the development and optimisation of AI avatar endorsement strategies in live-stream e-commerce. Full article

In recent years, the generation and animation of avatars in virtual reality (VR) have undergone a definitive paradigm shift, transitioning from pre-rendered, manually rigged meshes to autonomous, AI-driven digital entities. While individual algorithms have been extensively studied, there is a critical lack of comprehensive synthesis regarding how these generative models impact the broader sociotechnical ecosystem of Spatial Computing. To address this gap, this systematic literature review, conducted in accordance with PRISMA guidelines, analyzed 48 primary studies to evaluate the intersection of Generative AI, hardware architecture, human psychology, and digital ethics. The synthesis reveals a deeply interdependent ecosystem. While advanced neural rendering and diffusion models (RQ1) successfully bypass traditional 3D authoring bottlenecks, their pursuit of absolute visual fidelity severely antagonizes the thermal and latency constraints of standalone mobile hardware (RQ2). The literature demonstrates that failing to mitigate these bottlenecks through hardware–software co-design (e.g., specialized ASICs, gaze-contingent foveation) inevitably shatters the user’s sensorimotor loop, collapsing the sense of agency and triggering the Kinematic Uncanny Valley (RQ3). Furthermore, as these hyper-realistic avatars achieve kinematic autonomy, they introduce unprecedented sociotechnical vulnerabilities regarding spatial privacy, dataset bias, and post-mortem digital identity (RQ4). Ultimately, this review concludes that realizing a compelling and inclusive AI-driven Metaverse is no longer an isolated computer graphics challenge; it demands a rigorous, interdisciplinary paradigm shift where algorithms, silicon architectures, and cognitive psychology are inextricably co-designed under a foundational framework of digital ethics. Full article

Background/Objectives: This paper investigates the application of radial basis function (RBF) interpolation to adapt the Toyota Human Model for Safety (THUMS) version 6 finite element (FE) models to diverse anthropometric profiles using ANSUR II data. The research focuses on generating personalized human body models (HBMs) across 50th, 80th, and 98th percentiles for both sexes in standing and seated postures, evaluating mesh quality with quantitative metrics, and assessing posture-dependent transformations. Methods: The geometric accuracy for the standing configuration was quantified using DICE similarity coefficients and the 95th percentile Hausdorff distance (HD95). Results: While global whole-body DICE similarity averaged approximately 0.40 due to an inherent variability in distal limb positioning, regional analysis demonstrated strong volumetric overlap in the critical chest and torso regions with DICE values ranging from 0.80 to 0.88. Regional HD95 values were within 20–30 mm across most of the surface area. Surfaces distance analyses showed that more than 95% of the nodes were within $\pm 20$ mm of the target surfaces with the distribution centered near zero across all the percentiles. The mesh quality for both standing and seated morphs demonstrated low violation rates with the aspect ratio being 28% to 30%, while warpage, skewness and, Jacobian determinants were less than 15%. The seated morphs preserved anatomical alignment and posture despite mesh density differences between the postures. Conclusions: These findings indicate that the morphing process preserves anatomical fidelity while highlighting the need for further optimization to mitigate localized distortions in dynamic simulations. Full article

Three-dimensional Gaussian Splatting (3DGS) methods have achieved real-time rendering and high-precision modeling in 3D human avatar modeling. However, most existing methods perform Gaussian sphere cloning, splitting, and pruning without explicit geometric constraints in density control, resulting in inadequate detail reconstruction. To address this, we propose a Gaussian Adaptive Density Control method with SDF constraints (GADC-SDF). Specifically, we introduce the Signed Distance Field (SDF) as explicit geometric constraints for adaptive density control in 3DGS optimization. First, SDF voxel grids are constructed from the SMPL-X mesh. Then, SDF values are applied to prune spheres outside the human body, while additional Gaussian spheres are cloned around the human surface. Furthermore, SDF gradients are utilized to split more Gaussian spheres in detail-rich regions. Experiments conducted on X-Humans, UPB, and ZJU_MoCap datasets demonstrate that our method matches the performance of state-of-the-art baselines across most quantitative metrics while providing a modest improvement in perceptual quality. Full article

Reconstructing photorealistic and animatable whole-body avatars from monocular videos is a hot topic in computer vision and computer graphics. However, existing methods still face challenges due to the limited frequency response of single-scale geometry encodings and the instability of appearance modeling without an explicit surface anchor. In this paper, we present H²Avatar, a real-time framework that builds on a mesh-embedded 3D Gaussian representation guided by SMPL-X and disentangles geometry and appearance into hierarchical and hybrid components. For geometry, we propose a semantic-aware hierarchical encoding based on a multi-scale tri-plane pyramid, where features at different resolutions capture both global structure and high-frequency surface details such as clothing wrinkles. For appearance, we introduce a hybrid rendering strategy that anchors canonical colors using a learnable UV texture map, and complements it with a neural residual color branch conditioned on tri-plane features, pose embedding, and surface normals to model pose- and view-dependent shading variations. This design improves temporal stability and preserves identity details while enhancing photorealism under complex motions. Experiments on the NeuMan dataset demonstrate that H²Avatar consistently outperforms representative baselines across multiple sequences, outperforming ExAvatar by up to 0.66 dB in PSNR and reducing LPIPS by up to 16.3%. These results validate the effectiveness of hierarchical geometry encoding and texture-anchored hybrid appearance modeling. Full article

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality globally. While multi-modal artificial intelligence (AI) models offer significant predictive potential, their translation into routine clinical practice is delayed by the “black box” nature of complex algorithms and the fragmentation of heterogeneous data. We present LANTERN-XGB, a hierarchical machine learning workflow designed to bridge this gap by generating interpretable “digital human avatars” for precision oncology. The methodology employs a multi-stage scalable tree boosting system (XGBoost) architecture utilizing shapley additive explanations (SHAP) for rigorous hierarchical feature selection, missing value management, and patient-specific decision support. The workflow was developed and benchmarked using a retrospective cohort of 437 patients with clinical N0 NSCLC, followed by validation on a prospective dataset (n = 100) and an independent external dataset (n = 100). The pipeline integrates diverse data modalities to predict occult lymph node metastasis (OLM). LANTERN-XGB identified a robust consensus signature driven by non-linear interactions among CT textural fragmentation, PET metabolic heterogeneity, tumor density distribution, and systemic clinical modulators. Exploratory transcriptomic pathway analysis (GSVA) revealed that high-risk predictions strongly correlate with systemic molecular dysregulation, such as the enrichment of immune-inflammatory signaling and metabolic stress pathways. The model achieved robust discrimination in external validation (AUC ≈ 0.77), performing comparably to state-of-the-art nomogram benchmarks. Crucially, the LANTERN-XGB framework demonstrated superior utility in handling diagnostic ambiguity; local force plots allowed for the correct reclassification of “borderline” prediction by visualizing feature interactions that standard linear models fail to capture. LANTERN-XGB provides a validated, open-source framework that successfully balances predictive power with clinical transparency. By empowering clinicians to visualize and verify the logic behind AI predictions, this workflow offers a pragmatic path for integrating reliable multi-modal avatars into daily medical decision-making. Full article