VV-DASH: A Framework for Volumetric Video DASH Streaming
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
With the increasing demand for immersive experiences, volumetric video has emerged as a critical technology, offering users six degrees of freedom (6DoF) to fully explore three-dimensional scenes. However, despite significant advancements, there remains a lack of a comprehensive and flexible adaptive streaming framework capable of delivering volumetric video over dynamic network conditions. To address this gap, we present VV-DASH, an end-to-end framework for adaptive volumetric video streaming over DASH (Dynamic Adaptive Streaming over HTTP). Our framework covers the entire streaming pipeline, from video source to video playback. We propose a codec-agnostic DASH Volumetric Video (DVV) segment format that consolidates compressed video content into DASH-ready segments. This segmentation improves achievable streaming throughput by 13.2%, effectively reduces bandwidth demand, and enhances the achievable streaming bitrate by up to 37.8%. In summary, VV-DASH provides a practical, high-performance framework for scalable and adaptive volumetric video streaming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it