Vivaaha VR: Immersive Wedding Theme Selection Through Virtual Reality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The wedding planning process is often complex, involving numerous decisions related to themes, décor, music, and overall event aesthetics. Traditional methods rely on physical visits and photographs, which limit a client’s ability to fully visualize their wedding experience. Vivaaha VR introduces an immersive virtual reality platform that enables users to explore, customize, and experience their wedding themes interactively before the actual event. By integrating VR environments with modular theme selection, décor customization, music simulation, and interactive blessing animations, the system enhances decision-making, reduces planning errors, and improves overall user satisfaction. Experimental case studies, including traditional Indian and beach destination wedding themes, demonstrate that Vivaaha VR significantly improves engagement, planning efficiency, and user experience compared to conventional approaches. This paper presents the system architecture, implementation methodology, and performance evaluation of Vivaaha VR, highlighting its potential as a transformative tool in modern wedding event management.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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 it