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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".