A Non-Conventional Approach to the Conversion of 2D Video and Film Content to Stereoscopic 3D
Bibliographic record
Abstract
We will present a non-conventional approach and method for converting naturalistic (non-computer-generated) 2D video and film material to Stereoscopic 3D. We will present experimental evidence to show the efficacy of colour-based surrogate depth maps for automatic 2D-to-3D conversion aimed at small screen applications. We will then present a semi-automatic 2D-to-3D conversion system (CRC-DMEG), also based on surrogate depth maps, for the conversion of video and film contents for commercial projection on large cinema screens. A major advantage of CRC-DMEG is that it exploits the correlation between the 2D colour images and the surrogate depth maps to allow for direct manipulation of the depth of objects in a scene. Another major advantage is that it allows for the instant realization of depth details, such as raindrops, foliage and the textures found in carpets. Our approach and method, minimizes the labor-intensive work associated with the conventional method of rotoscoping. Moreover, the manual task of filling in disoccluded regions is also significantly reduced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".