Generative AI for Immersive Video: Recent Advances and Future Opportunities
Bibliographic record
Abstract
Immersive video serves as a key component of eXtended Reality (XR) that aims to create and interact with simulated virtual or hybrid environments. Such a technology allows users to experience immersive sensations that transcend time and space, and meanwhile continuously providing training data for emerging technologies like Embodied AI. Thanks to the advancements in sensing, computing, and display, recent years have witnessed many excellent works for XR and related hardware or software systems. However, challenges like high creation cost, lack of immersion, and limited scalability hinder the practical application of immersive video services. Whilst recently emerged generative artificial intelligence (GenAI) provides us with new insights in tackling existing challenges. In this paper, we conduct a comprehensive survey into the recent advances and future opportunities on how GenAI can benefit immersive video services. By introducing a systematic taxonomy, we meticulously classify the pertinent techniques and applications into three well-defined categories aligned with the pipeline of immersive video service: content creation, network delivery, and client-side display. This categorization enables a structured exploration of the diverse roles on how GenAI can benefit immersive video service, providing a framework for a more comprehensive understanding and evaluation of these technologies. To the best of our knowledge, this work is the first systematic survey of GenAI in XR settings, laying a foundation for future research in this interdisciplinary domain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".