A semi-automatic 2D to stereoscopic 3D image and video conversion system in a semi-automated segmentation perspective
Bibliographic record
Abstract
We create a system for semi-automatically converting unconstrained 2D images and videos into stereoscopic 3D. Current efforts are done automatically or manually by rotoscopers. The former prohibits user intervention, or error correction, while the latter is time consuming, requiring a large staff. Semi-automatic mixes the two, allowing for faster and accurate conversion, while decreasing time to release 3D content. User-defined strokes for the image, or over several keyframes, corresponding to a rough estimate of the scene depths are defined. After, the rest of the depths are found, creating depth maps to generate stereoscopic 3D content, and Depth Image Based Rendering is employed to generate the artificial views. Here, depth map estimation can be considered as a multi-label segmentation problem, where each class is a depth value. Optionally, for video, only the first frame can be labelled, and the strokes are propagated using a modified robust tracking algorithm. Our work combines the merits of two respected segmentation algorithms: Graph Cuts and Random Walks. The diffusion of depths from Random Walks, combined with the edge preserving properties from Graph Cuts is employed to create the best results possible. Results demonstrate good quality stereoscopic images and videos with minimal effort.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".