Avatar: a virtual reality based tool for collaborative production of theater shows
Bibliographic record
Abstract
One of the more important limitations of actual tools for performing arts production and design is that collaboration between designers is hard to achieve. In fact, designers must actually be co-located to collaborate in the design of a show, something that is not always possible. While teleconference tools could be used to partially solve this problem, this solution offers no direct interactivity and no synchronization between designers. Also some problems like perspective effects and single viewpoint constrained by the camera are inherent to this solution. Specialized software for performing arts design (e.g. "Life Forms") do not generally provide real-time collaboration and are not really convenient for collaborative work. Also, these systems are often expensive and complex to operate. A more adapted solution combining concepts from virtual reality, network technology, and computer vision has then been specifically developed for collaborative work by performing arts designers. This paper presents a virtual reality application for supporting distributed collaborative production of theater shows resulting from our research. Among other constraints, this application has to ensure that the virtual scene that is being shared between multiple designers is always in sync (by use of computer vision) with a real counterpart and that this synchronization is achieved in real-time. Also, system cost must be kept as low as possible, platform independence must be achieved whenever possible and, since it is to be used by people that are not computer experts, the application has to be user-friendly.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".