XCam: Mixed-Initiative Virtual Cinematography for Live Production of Virtual Reality Experiences
Bibliographic record
Abstract
VR is often utilized for organizing virtual events such as meetings, conferences, and concerts; however, support for live production is lacking in most existing VR tools.We present XCam, a toolkit enabling mixed-initiative control over virtual camera systems-from fully manual control by users to increasingly automated, systemdriven control with minimal user intervention.XCam's architectural design separates the concerns of object tracking, camera motion, and scene transition, giving more degrees of freedom to operators who can adjust the level of automation along all three dimensions.We used XCam to conduct two studies: (1) interviews with six VR content creators probe into what aspects should and shouldn't be automated based on six applications developed with XCam; (2) three workshops with experts explore XCam's utility in live production of an interactive VR flm sequence, a lecture on cinematography, and an alumni meeting in social VR.Expert feedback from our studies suggests how to balance automation and control, and the opportunities and limits of future AI-driven tools.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".