VisConductor: Affect-Varying Widgets for Animated Data Storytelling in Gesture-Aware Augmented Video Presentation
Bibliographic record
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.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".