Editing Soft Shadows in a Digital Photograph
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
In this article, we develop tools for shadow modification in images where a shadowed region is characterized by soft boundaries with varying sharpness along the shadow edges. Modeling shadow edges presents an interesting challenge because they can vary from infinitely sharp edges for shadows produced by a point light source to extremely soft edges for shadows produced by large area light sources. We propose an entirely image-based shadow editing tool for a single-input image. This technique for modeling, editing, and rendering shadow edges in a photograph or a synthetic image lets users separate the shadow from the rest of the image and make arbitrary adjustments to its position, sharpness, and intensity. These machine-adjustable photographs can offer interactivity that might improve images' expressiveness and help us investigate the influence of boundary sharpness on the perception of object-to-object contact, as well as understand how humans assess shadows to estimate object height above a ground plane
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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