A Study on Color Emotion Mapping and Character Matching in Animated Films Based on Fuzzy Logic
Why this work is in the frame
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Bibliographic record
Abstract
As the most intuitive visual phenomenon of animated films, color has emotional characteristics that are closely related to the viewers' emotional experience.From the perspective of chromaticity and psychology, we explain the method of color emotion quantification, calculate the fuzzy affiliation degree and grey correlation degree for the uncertainty and fuzziness between color and emotion mapping, put forward the method of fuzzy grey correlation for emotion mapping in animated movies, and carry out the experiment of color emotion mapping in animated movies.Through the experiment, it is found that the character color schemes of warm, cold and neutral colors are suitable for the design of character color emotion experience in animated movies.Taking the animated film "Ne Zha: The Descent of the Magic Boy" as the research object, the correlation between color emotion mapping and character matching is further explored.Most of the H-value color blocks in Ne Zha are distributed between 0-60, which indicates warm and neutral tones, and the distribution of S-value and V-value color blocks shows a clear trend of decreasing color saturation, while the overall luminance remains basically stable.The whole film takes the proportion of red, blue, color purity changes and other aspects of color design to achieve the position of the characters, the character of the transformation of the transformation of the matching and implied.
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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) | 0.000 | 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 it