A Reusable Scripting Engine for Automating Cinematics and Cut-Scenes in Video Games
Bibliographic record
Abstract
Storytelling can play a critical role in the success of modern video games. Unfortunately, it can often be quite difficult for storytellers to directly craft content for games, typically requiring them to work with programmers to implement story elements. This needlessly complicates the development process, straining scarce resources while potentially hampering creativity and story quality at the same time. As a result, supports and tools are necessary to enable storytellers to generate story content for games directly, with minimal programming or programmer assistance required, if any. This paper introduces a Reusable Scripting Engine to automate the generation of cinematics and cut-scenes in video games. This approach allows storytellers to provide their stories in a well-defined, structured format, which is then interpreted by our engine, along with supplemental graphic and audio content, to produce an animated presentation of the story in an automated fashion. This paper presents the design of our Reusable Scripting Engine, and discusses a prototype implementation of this design, as well as initial experiences with using this prototype system to date.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".