Bibliographic record
Abstract
Due to the rapid pace of digitalization, Virtual Production (VP) in film is gaining importance. With this gamified production process, live-action and computer graphics can be combined in real-time while filming on set. This paper focuses on an interdisciplinary research project that investigates the effects of VP on visual aesthetics, on the changing workflows of filmmakers and actors, and on the perception of a cinema audience. To systematically compare conventional filmmaking with new virtual forms of production, two short feature films were shot both conventionally (in real locations) and virtually (in the digitally scanned versions of these locations). The filmmakers aspired to keep all parameters of the production the same so that wherever possible, the only differences would be in terms of spatial representation. The process of VP included shooting with green-screen and pre-visualization based on real-time image rendering in a moderate quality. The high-resolution variants, however, were still processed in post-production. The methodology comprised a combination of qualitative, practice-based research and quantitative, empirical approaches, in the tradition of mixed methods. As VP continues to develop, green-screens are being replaced by large arrays of LED-displays, as in, for example, The Mandalorian. The present study shows that in the first phase of VP, in which green-screen procedures are still predominant, composition artifacts occur mainly in the context of moderate production resources and are still measurable in terms of image quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.114 | 0.018 |
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".