Photorealistic Monocular Gaze Redirection Using Machine Learning
Why this work is in the frame
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Bibliographic record
Abstract
We propose a general approach to the gaze redirection problem in images that utilizes machine learning. The idea is to learn to re-synthesize images by training on pairs of images with known disparities between gaze directions. We show that such learning-based re-synthesis can achieve convincing gaze redirection based on monocular input, and that the learned systems generalize well to people and imaging conditions unseen during training. We describe and compare three instantiations of our idea. The first system is based on efficient decision forest predictors and redirects the gaze by a fixed angle in real-time (on a single CPU), being particularly suitable for the videoconferencing gaze correction. The second system is based on a deep architecture and allows gaze redirection by a range of angles. The second system achieves higher photorealism, while being several times slower. The third system is based on real-time decision forests at test time, while using the supervision from a "teacher" deep network during training. The third system approaches the quality of a teacher network in our experiments, and thus provides a highly realistic real-time monocular solution to the gaze correction problem. We present in-depth assessment and comparisons of the proposed systems based on quantitative measurements and a user study.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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