Towards Low Latency Multi-viewpoint 360° Interactive Video: A Multimodal Deep Reinforcement Learning Approach
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
Recently, the fusion of 360° video and multi-viewpoint video, called multi-viewpoint (MVP) 360° interactive video, has emerged and created much more immersive and interactive user experience, but calls for a low latency solution to request the high-definition contents. Such viewing-related features as head movement have been recently studied, but several key issues still need to be addressed. On the viewer side, it is not clear how to effectively integrate different types of viewing-related features. At the session level, questions such as how to optimize the video quality under dynamic networking conditions and how to build an end-to-end mapping between these features and the quality selection remain to be answered. The solutions to these questions are further complicated given the many practical challenges, e.g., incomplete feature extraction and inaccurate prediction.This paper presents an architecture, called iView, to address the aforementioned issues in an MVP 360° interactive video scenario. To fully understand the viewing-related features and provide a one-step solution, we advocate multimodal learning and deep reinforcement learning in the design. iView intelligently determines video quality and reduces the latency without pre-programmed models or assumptions. We have evaluated iView with multiple real-world video and network datasets. The results showed that our solution effectively utilizes the features of video frames, networking throughput, head movements, and viewpoint selections, achieving at least 27.2%, 15.4%, and 2.8% improvements on the three video datasets, respectively, compared with several state-of-the-art methods.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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