Meet-in-Style: Text-Driven Real-Time Video Stylization Using Diffusion Models
Bibliographic record
Abstract
We present Meet-in-Style-a new approach to real-time stylization of live video streams using text prompts. In contrast to previous text-based techniques, our system is able to stylize input video at 30 fps on commodity graphics hardware while preserving structural consistency of the stylized sequence and minimizing temporal flicker. A key idea of our approach is to combine diffusion-based image stylization with a few-shot patch-based training strategy that can produce a custom image-to-image stylization network with real-time inference capabilities. Such a combination not only allows for fast stylization, but also greatly improves consistency of individual stylized frames compared to a scenario where diffusion is applied to each video frame separately. We conducted a number of user experiments in which we found our approach to be particularly useful in video conference scenarios enabling participants to interactively apply different visual styles to themselves (or to each other) to enhance the overall chatting experience.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".