XCam: Mixed-Initiative Virtual Cinematography for Live Production of Virtual Reality Experiences

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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Published
2025-04-25
DOI
https://doi.org/10.1145/3706598.3713305
Primary Topic
Video Analysis and Summarization
Type
article
Field-Weighted Citation Impact
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article

XCam: Mixed-Initiative Virtual Cinematography for Live Production of Virtual Reality Experiences

Michael Nebeling, Hanuma Teja Maddali, Liwei Wu
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Video Analysis and Summarization
article

XCam: Mixed-Initiative Virtual Cinematography for Live Production of Virtual Reality Experiences

Michael Nebeling, Hanuma Teja Maddali, Liwei Wu
article en

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.

University of Waterloo (CA), University of Michigan (US), University of Maryland, College Park (US)
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Openalex Percentile: Top 93%
Video Analysis and Summarization
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