Interactive floating windows: a new technique for stereoscopic video games
Bibliographic record
Abstract
The film industry has a long history of creating compelling experiences in stereoscopic 3D. Recently, the video game as an artistic medium has matured into an effective way to tell engaging and immersive stories. Given the current push to bring stereoscopic 3D technology into the consumer market there is considerable interest to develop stereoscopic 3D video games. Game developers have largely ignored the need to design their games specifically for stereoscopic 3D and have thus relied on automatic conversion and driver technology. Game developers need to evaluate solutions used in other media, such as film, to correct perceptual problems such as window violations, and modify or create new solutions to work within an interactive framework. In this paper we extend the dynamic floating window technique into the interactive domain enabling the player to position a virtual window in space. Interactively changing the position, size, and the 3D rotation of the virtual window, objects can be made to 'break the mask' dramatically enhancing the stereoscopic effect. By demonstrating that solutions from the film industry can be extended into the interactive space, it is our hope that this initiates further discussion in the game development community to strengthen their story-telling mechanisms in stereoscopic 3D games.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".