A Nonconventional Approach to the Conversion of 2D Video and Film Content to Stereoscopic 3D
Bibliographic record
Abstract
This paper presents a nonconventional approach and method for converting naturalistic (non–computer-generated) two-dimensional (2D) video and film material to Stereoscopic 3D. Experimental evidence is presented to show the efficacy of color-based surrogate depth maps for automatic 2D-to-3D conversion aimed at small-screen applications. A semiautomatic 2D-to-3D conversion system (CRC-DMEG), also based on surrogate depth maps, for the conversion of video and film content for commercial projection on large cinema screens is then presented. A major advantage of CRC-DMEG is that it exploits the correlation between the 2D color 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 instant realization of depth details, such as raindrops, foliage, and textures found in carpets. This 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) | 0.007 | 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".