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Innovation at the Intersection of AI and XR: Opportunities for AI to enhance XR experiences, including photorealistic avatars, dynamic environments, and anticipatory user interactions. XR for AI: Using XR to train and simulate AI, offering controlled yet realistic environments for AI learning and evolution: visualizing and interacting with complex AI models, enhancing understanding, and facilitating innovative training environments. Technical and Human Challenges: Addressing the obstacles and human factors critical to the advancement of AI in XR, including system performance, user engagement, and intuitive interfaces. Ethical Considerations in AI-driven XR: Exploring the implications of AI advancements in XR on privacy, agency, digital identity, and broader societal impacts. Industry Relevance: Highlighting technologies, methodologies, applications, prototypes or experiences of clear industry relevance. Concrete areas of interest for the technical program include but are not limited to: Systems, including techniques, performance, and implementation: System components, virtual reality platforms, AI platforms for VR/AR, cloud-based platforms, Data generation, manipulation, analysis, and validation, Tracking, physical environment mapping, registration, Vision for VR/AR, deep learning for VR/AR, Standards and theoretical models for AI and/or VR Content creation and modeling: Generation of immersive environments and virtual worlds, Environments for gaming, simulation, training, Visualisation, optimized and realistic rendering, Geometric modeling and design in immersive settings, Animations, crowd-simulation, character modeling, Customisation and personalization (e.g., for training) Cognitive aspects, perception, user behavior: Semantic and cognitive aspects of virtual reality, Depth perception, multimodal perception, Behaviour and activity generation, Representations of self (avatars), embodiment, presence, Virtual agents, conversational non-player characters (NPCs), Understanding and modeling human behavior, emotions AI technologies for VR/AR: Search, planning, reasoning, Knowledge representation, Natural language processing, Robotics and perception, Multi-agent systems, Statistical learning, Deep learning XR for AI: Visualizing Complex AI models in XR; training AI Models through XR simulations of synthetic data; enhancing human-AI synergy with XR; XR as a testbed for AI Ethics and behavior; AI-driven content creation in XR; XR for AI-driven Robotics and Autonomous systems. Interactions / interactive and responsive environments: Multimodal interaction and experiences in VR/AR, Machine learning for multimodal interaction, generative AI and LLMs in virtual environments, Human-virtual user/agent interaction, Human to human communication in virtual environments, collaboration and communication, Dialogue modeling and generation, conversational and natural language interfaces, speech interaction for AR/VR, Navigation and spatial orientation in VR, Interaction devices, Brain-Computer Interfaces (BCI) Applications and use cases: Data and knowledge representation, problem-solving, Visualization concepts (including, e.g., spatial visualization, multimodality for visualization) and domains (e.g., scientific visualization), Arts, leisure, and entertainment, Gaming and game narratives, immersive storytelling and gameplay, Education, training, simulation, Business, prototyping, productivity, design and architecture, evaluation, Telepresence and collaboration, social interactions, Healthcare and therapy, Evaluation metrics and methodologies, Quality of Experience (QoE) Ethical and societal aspects of AI and VR/AR: Sensory vulnerability, Privacy and data, Social isolation, desensitization, Overestimation of abilities, psychiatric, Unpalatable fantasies, torture/virtual criminality, Manipulation, appropriate roaming, and re-creation Industry-focused contributions: Technologies, methodologies, applications, prototypes, and experiences of clear industry relevance</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-04-15</div> </div> </div>  <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-th"></i> Related Conferences</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw3" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw3_c0">CCF</th><th 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class="keys" style="display:none" title="/conference/2790"><span>4393</span><span>3271</span><span>3882</span><span>4410</span><span>1277</span><span>3978</span><span>1550</span><span>4602</span><span>3977</span><span>4295</span></div> </div></div> </div> </div> <div class="portlet" id="yw5"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-file"></i> Related Journals</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw6" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw6_c0">CCF</th><th id="yw6_c1">Full Name</th><th id="yw6_c2">Impact Factor</th><th id="yw6_c3">Publisher</th><th id="yw6_c4">ISSN</th></tr> </thead> <tbody> <tr class="odd"> <td></td><td><a href="/journal/123">SIGMOD Record</a></td><td> </td><td>ACM</td><td>0163-5808</td></tr> <tr class="even"> <td></td><td><a href="/journal/760">Biomedical Signal Processing and 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