Movie Editing and Cognitive Event Segmentation in Virtual Reality Video
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Abstract (arXiv)
Traditional cinematography has relied for over a century on a well-established set of editing rules, called continuity editing, to create a sense of situational continuity. Despite massive changes in visual content across cuts, viewers in general experience no trouble perceiving the discontinuous flow of information as a coherent set of events. However, Virtual Reality (VR) movies are intrinsically different from traditional movies in that the viewer controls the camera orientation at all times. As a consequence, common editing techniques that rely on camera orientations, zooms, etc., cannot be used. In this paper we investigate key relevant questions to understand how well traditional movie editing carries over to VR. To do so, we rely on recent cognition studies and the event segmentation theory, which states that our brains segment continuous actions into a series of discrete, meaningful events. We first replicate one of these studies to assess whether the predictions of such theory can be applied to VR. We next gather gaze data from viewers watching VR videos containing different edits with varying parameters, and provide the first systematic analysis of viewers' behavior and the perception of continuity in VR. From this analysis we make a series of relevant findings; for instance, our data suggests that predictions from the cognitive event segmentation theory are useful guides for VR editing; that different types of edits are equally well understood in terms of continuity; and that spatial misalignments between regions of interest at the edit boundaries favor a more exploratory behavior even after viewers have fixated on a new region of interest. In addition, we propose a number of metrics to describe viewers' attentional behavior in VR. We believe the insights derived from our work can be useful as guidelines for VR content creation.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Jun 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1.
5 papers.
- 2021ScanGAN360: A Generative Model of Realistic Scanpaths for 360circ Imagescited by 23
- 2022Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360deg Omnidirectional Imagecited by 13
- 2023Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challengescited by 12
- 2024Reframe Anything: LLM Agent for Open World Video Reframingcited by 4
- 2023Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentationcited by 3
- 2024Reframe Anything: LLM Agent for Open World Video Reframing10 Mar 2024
- 2023Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges23 May 2023
- 2023Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation18 Apr 2023
- 2022Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360deg Omnidirectional Image23 Dec 2022
- 2021ScanGAN360: A Generative Model of Realistic Scanpaths for 360circ Images25 Mar 2021