High-quality object-space dynamic ambient occlusion for characters using Bi-level regression
Bibliographic record
Abstract
The widely used ambient occlusion (AO) technique provides an approximation of some global illumination effects and is efficient enough for use in real-time applications. Because it relies on computing the visibility from each point on a surface, AO computation is expensive for dynamically deforming objects, such as characters in particular. In this paper, we describe an algorithm for producing high-quality dynamically changing AO for characters. Our fundamental idea is to factorize the AO computation into a coarse-scale component in which visibility is determined by approximating spheres, and a fine-scale component that leverages a skinning-like algorithm for efficiency, with both components trained in a regression against ground-truth AO values. The resulting algorithm accommodates interactions with external objects and generalizes without requiring carefully constructed training data. Extensive comparisons illustrate the capabilities and advantages of our algorithm.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".