Collaborative view synthesis for interactive multi-view video streaming
Bibliographic record
Abstract
Interactive multi-view video enables users to enjoy the video from different viewpoints. Yet multi-view dramatically in-creases the video data volume and their computation, mak-ing realtime transmission and interactions a challenging task. It therefore calls for efficient view synthesis strategies that flexibly generate visual views. In this paper, we present a collaborative view synthesis strategy for online interactive multi-view video streaming based on Depth-Image Based Rendering (DIBR) view synthesis technology, which gener-ates a visual view with the texture and depth information on both sides. Different from the traditional DIBR algorithm for single view synthesis, we explore the collaboration rela-tionship between different viewpoints synthesis for a range of visual views generation, and propose Shift DIBR (S-DIBR). In S-DIBR, only the projected pixels, rather than all the pixels of the reference view, are utilized for next visual view generation. Therefore, the computation complexity of pro-jection transform, which is the most computation intensive process in the traditional DIBR algorithm, is reduced to fulfill the requirement of online interactive streaming. Ex-periment results validate the efficiency of our collaborative view synthesis strategy, as well as the bandwidth scalability of the streaming system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".