Avatar: a virtual reality based tool for collaborative production of theater shows
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it