VisConductor: Affect-Varying Widgets for Animated Data Storytelling in Gesture-Aware Augmented Video Presentation
June 25, 2024 Β· Declared Dead Β· π Proc. ACM Hum. Comput. Interact.
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Temiloluwa Femi-Gege, Matthew Brehmer, Jian Zhao
arXiv ID
2406.17986
Category
cs.HC: Human-Computer Interaction
Citations
7
Venue
Proc. ACM Hum. Comput. Interact.
Last Checked
4 months ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted