Bibliographic record
Abstract
This paper will interrogate and discuss the possibility of applying machine learning technology into storyboarding processes from an animation and film industry perspective. In current animation or film production studios, most of a storyboarder's creative process is a repeating step of manually editing and visualizing content from a script. It is a storyboarders’ responsibility to analyze camera cuts and scenes information, then organize the scenes, the director's notes, and camera movements in storyboard drawing software to create a working template. The storyboarder is always responsible for hundreds of such repeating steps in storyboarding processes. Those repeating actions are all inefficient and could be limiting. This paper will analyze and review many machine learning technology methods in the current animation and film industry. The literature review part will identify many machine learning methods’ positions in the animation production pipeline and their advantages and disadvantages in related professional fields. Finally, this research paper will address a theoretical concept of applying visual recognition, text mining and automation technology into storyboarding software to analyze the scripts for storyboarders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".