Perception of blending in stereo motion panoramas
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
Most methods for synthesizing panoramas assume that the scene is static. A few methods have been proposed for synthesizing stereo or motion panoramas, but there has been little attempt to synthesize panoramas that have both stereo and motion. One faces several challenges in synthesizing stereo motion panoramas, for example, to ensure temporal synchronization between left and right views in each frame, to avoid spatial distortion of moving objects, and to continuously loop the video in time. We have recently developed a stereo motion panorama method that tries to address some of these challenges. The method blends space-time regions of a video XYT volume, such that the blending regions are distinct and translate over time. This article presents a perception experiment that evaluates certain aspects of the method, namely how well observers can detect such blending regions. We measure detection time thresholds for different blending widths and for different scenes, and for monoscopic versus stereoscopic videos. Our results suggest that blending may be more effective in image regions that do not contain coherent moving objects that can be tracked over time. For example, we found moving water and partly transparent smoke were more effectively blended than swaying branches. We also found that performance in the task was roughly the same for mono versus stereo videos.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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