Time slice video synthesis by robust video alignment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Time slice photography is a popular effect that visualizes the passing of time by aligning and stitching multiple images capturing the same scene at different times together into a single image. Extending this effect to video is a difficult problem, and one where existing solutions have only had limited success. In this paper, we propose an easy-to-use and robust system for creating time slice videos from a wide variety of consumer videos. The main technical challenge we address is how to align videos taken at different times with substantially different appearances, in the presence of moving objects and moving cameras with slightly different trajectories. To achieve a temporally stable alignment, we perform a mixed 2D-3D alignment, where a rough 3D reconstruction is used to generate sparse constraints that are integrated into a pixelwise 2D registration. We apply our method to a number of challenging scenarios, and show that we can achieve a higher quality registration than prior work. We propose a 3D user interface that allows the user to easily specify how multiple videos should be composited in space and time. Finally, we show that our alignment method can be applied in more general video editing and compositing tasks, such as object removal.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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