How Example-Based Authoring of Motion Graphics Impacts Creative Expression: Differences in Perceptions of Professional and Casual Motion Designers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Motion graphics authoring is a time-intensive endeavor, demanding proficiency in various feature-rich software. Automated, example-based solutions are now being explored to simplify the motion graphics creation process. To investigate how such streamlined authoring tools impact motion designers’ workflows and perceptions of creativity, we deployed an end-to-end motion graphics authoring tool to 14 users, spanning casual to professional design expertise. Our key findings reveal a dichotomy: casual designers embraced the tool’s automation, finding empowerment in its simplicity, even at the expense of losing narrative control. Conversely, professionals expressed reservations and raised concerns about the trade-offs between efficiency and creative autonomy. Notably, the level of automation in animation emerged as a point of contention, underscoring differing expectations between the two groups. Our work contributes insights into such nuances, offering implications for designing the next generation of motion graphics authoring tools that cater to a broad spectrum of creative aspirations and abilities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it