A Tele-immersive System Based On Binocular View Interpolation
Why this work is in the frame
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Bibliographic record
Abstract
The main idea behind tele-immersive environment is to create an immersive virtual environment that connect people across networks and enable them to interact not only with each other, but also with various other forms of shared digital data (video, 3D models, images, text, etc.). Tele-immersive environments may eventually replace current video and telephone conferencing, and enable for a better and more intuitive way to communicate between people and computer systems. To accomplish this, participants to a meeting has to be represented digitally with a high degree of accuracy in order to keep a sense of immersion. Tele-immersive environments should have the same "feel" as a real meeting. Interactions among people should be natural. In other to create such a system, we need to solve the key problem of how to create in real-time new views from a fixed network of cameras that will correspond to new viewpoints. We also need to do this for two virtual cameras corresponding to the inter-ocular distance of each participant. In this paper, we will describe a new binocular view interpolation method based on a re-projection technique using calibrated cameras. We will discuss the various aspects of this new algorithm and of the hardware systems necessary to perform these operations in real-time. We will also present early experimental results illustrating the various advantages of this algorithm.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it