Immersive Technologies
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Democratisation of XR content creation using generative AI

Recently, GenAI and other AI-based tools have emerged to support XR, mainly VR, content creation. This development reshapes and democratises content creation by lowering technical barriers and costs and enabling faster development and deployment.

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The tools empower actors, such as independent creators, enterprises, educators, and researchers, to prototype ideas and produce 3D-constructed assets and virtual worlds faster, more efficiently, and at scale, without extensive technical skills.

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In other words, AI is facilitating low-code to no-code XR creations for everyone in the community. The adoption is gradual and use-case dependent. Challenges emerge surrounding copyright, content ownership, skill development in creative design and content creation, and excessive energy consumption.

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Maturity

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More diversified access to large AI models Increase in co-evolution between hardware and AI Collaboration between humans and AI Democratisation of XR content creation using generative AI Public investments in XR continue to increase Standardisation of data space architectures for secure and trusted data sharing Growing significance of augmented data management Trusted digital recognitions are emerging Digital Trust Frameworks: from hierarchical to distributed trust Emerging dualuse of AI in cybersecurity More accessible privacy-enhancing technologies to share data Rising pressure to prepare for post-quantum cryptography Emergence of measures for secure Internet of Things The growing need for security protocols to work in the cloud Growing investments in the European cloud infrastructure & sovereignty More AI-driven hardware in data centres to manage workloads in the cloud Increasing need for heterogeneous and hybrid computing platforms Trade-offs in accuracy with approximate computing Fixed wireless access Fibre optic networks: strategic infrastructure for the knowledge economy Enhancing quantum computing robustness and potential Quantum sensing integration to other domains

Drivers

Impact

education

Education

  • The AI-XR convergence trend supports faster creation of bespoke XR learning environments once the visual fidelity performance is met by XR platforms and applications for education.
  • Despite the low-code and no-code, teachers might still require assistance and support from experts and skilled professionals to guide students.
Research

Research

  • The democratisation of AI-enabled XR content can support rapid prototyping of experimental XR-based setups and access to graphical and 3D-modelled assets for research.
Operations

Operations

  • Lower production costs for simulations require high levels of functional validation (for example, safety-critical simulations).
  • Facilitating platforms for creation and deployment of XR emerge.
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