LiV: Live DASH Streaming for Volumetric Video
Bibliographic record
Abstract
Volumetric video is transforming immersive media and remote interaction, enabling applications ranging from holographic telepresence to augmented reality concerts. Despite its potential across various industries, real-time delivery is challenged by substantial data volume and stringent requirements for low-latency, high-quality streaming. Previous research has addressed these issues through improved compression efficiency, faster encoding and decoding processes, and adaptive streaming protocols to address network variability. However, a practical solution for live volumetric video streaming remains elusive. This paper introduces LiV, a live Dynamic Adaptive Streaming over HTTP (DASH) system specifically designed for general volumetric video. LiV effectively balances bandwidth demands and computational efficiency, facilitating stall-free playback and high visual quality. Leveraging a parallelized execution of the Draco encoder and decoder, our evaluations demonstrate that LiV enables smooth, real-time streaming with enhanced visual fidelity within the user’s field-of-view (FoV). LiV also significantly reduces the bandwidth demand by up to 30%. These findings mark a significant step toward practical live volumetric video with broad implications for immersive media.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".