From Prompt to Metaverse: User Perceptions of Personalized Spaces Crafted by Generative AI
Bibliographic record
Abstract
Generative artificial intelligence (AI) has revolutionized content creation. In parallel, the Metaverse has emerged to transcend the constraints of our physical reality. While Generative AI has a multitude of exciting applications for the fields of writing, coding, and graphic design, its usage to personalize our virtual space has not yet been explored. In this paper, we investigate the application of Artificial Intelligence Generated Content (AIGC) to personalize our virtual spaces and enhance the metaverse experience. To this end, we present a pipeline to enable users to customize their virtual spaces. Moreover, we explore the hardware resources and latency required for personalized spaces, as well as user acceptance of the AI-generated spaces. Comprehensive user studies follow extensive system experiments. Our research evaluates users' perceptions of two generated spaces: panoramic images and 3D virtual spaces. According to our findings, users have shown a great interest in 3D personalized spaces, and the practicality and immersion of 3D space generation tools surpass panoramic space generation tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".