Interactive animation of the eye region
Bibliographic record
Abstract
Humans are extremely sensitive to facial realism and spend a surprisingly amount of time focusing their attention on other people's faces. Thus, believable human character animation requires realistic facial performance. Various techniques have been developed to capture highly detailed actor performance or to help drive facial animation. However, the eye region remains a largely unexplored field and automatic animation of this region is still an open problem. We tackle two different aspects of automatically generating facial features, aiming to recreate the small intricacies of the eye region in real-time. First, we present a system for real-time animation of eyes that can be interactively controlled using a small number of animation parameters, including gaze. These parameters can be obtained using traditional animation curves, measured from an actor’s performance using off-the-shelf eye tracking methods, or estimated from the scene observed by the character using behavioral models of human vision. We present a model of eye movement, that includes not only movement of the globes, but also of the eyelids and other soft tissues in the eye region. To our knowledge this is the first system for real-time animation of soft tissue movement around the eyes based on gaze input. Second, we present a method for real-time generation of distance fields for any mesh in screen space. This method does not depend on object complexity or shape, being only contained by the intended field resolution. We procedurally generate lacrimal lakes on a human character using the generated distance field as input. We present different sampling algorithms for surface exploration and distance estimation, and compare their performance. To our knowledge this is the first method for real-time or screen space generation of distance fields.
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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 | 0.000 |
| 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 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".