StyleBin: Stylizing Video by Example in Stereo
Bibliographic record
Abstract
In this paper we present StyleBin—an approach to example-based stylization of videos that can produce consistent binocular depiction of stylized content on stereoscopic displays. Given the target sequence and a set of stylized keyframes accompanied by information about depth in the scene, we formulate an optimization problem that converts the target video into a pair of stylized sequences, in which each frame consists of a set of seamlessly stitched patches taken from the original stylized keyframe. The aim of the optimization process is to align the individual patches so that they respect the semantics of the given target scene, while at the same time also following the prescribed local disparity in the corresponding viewpoints and being consistent in time. In contrast to previous depth-aware style transfer techniques, our approach is the first that can deliver semantically meaningful stylization and preserve essential visual characteristics of the given artistic media. We demonstrate the practical utility of the proposed method in various stylization use cases.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".