How Example-Based Authoring of Motion Graphics Impacts Creative Expression: Differences in Perceptions of Professional and Casual Motion Designers
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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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".