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VisConductor: Affect-Varying Widgets for Animated Data Storytelling in Gesture-Aware Augmented Video Presentation

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arxiv 2406.17986 v1 pith:COZ3UGHB submitted 2024-06-25 cs.HC

classification cs.HC
keywords gesturespresentationpresentersvisconductoraugmentedcontentdatapresentations
verification ladder T0 review T1 audit T2 compute T3 formal
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Augmented video presentation tools provide a natural way for presenters to interact with their content, resulting in engaging experiences for remote audiences, such as when a presenter uses hand gestures to manipulate and direct attention to visual aids overlaid on their webcam feed. However, authoring and customizing these presentations can be challenging, particularly when presenting dynamic data visualization (i.e., animated charts). To this end, we introduce VisConductor, an authoring and presentation tool that equips presenters with the ability to configure gestures that control affect-varying visualization animation, foreshadow visualization transitions, direct attention to notable data points, and animate the disclosure of annotations. These gestures are integrated into configurable widgets, allowing presenters to trigger content transformations by executing gestures within widget boundaries, with feedback visible only to them. Altogether, our palette of widgets provides a level of flexibility appropriate for improvisational presentations and ad-hoc content transformations, such as when responding to audience engagement. To evaluate VisConductor, we conducted two studies focusing on presenters (N = 11) and audience members (N = 11). Our findings indicate that our approach taken with VisConductor can facilitate interactive and engaging remote presentations with dynamic visual aids. Reflecting on our findings, we also offer insights to inform the future of augmented video presentation tools.

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Cited by 1 Pith paper

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  1. Reflecting on Design Paradigms of Animated Data Video Tools

    cs.HC 2025-02 conditional novelty 6.0 of 10

    This CHI paper proposes a two-dimensional framework for classifying 46 data video authoring tools and summarizes design paradigms across components and human-AI roles.

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