Expressive Animated Character Sequences Using Knowledge-Based Painterly Rendering
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
We propose a technique to enhance emotional expressiveness in games and animations. Artists have used colors and painting techniques to convey emotions in their paintings for many years. Moreover, researchers have found that colors and line properties affect users' emotions. We propose using painterly rendering for character sequences in games and animations with a knowledge-based approach. This technique is especially useful for parametric facial sequences. We introduce two parametric authoring tools for animation and painterly rendering and a method to integrate them into a knowledge-based painterly rendering system. Furthermore, we present the results of a preliminary study on using this technique for facial expressions in still images. The results of the study show the effect of different color palettes on the intensity perceived for an emotion by users. The proposed technique can provide the animator with a depiction tool to enhance the emotional content of a character sequence in games and animations.
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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 | 0.000 | 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) | 0.002 | 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