Meet-in-Style: Text-Driven Real-Time Video Stylization Using Diffusion Models
Why this work is in the frame
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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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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it