Katika: An End-to-End System for Authoring Amateur Explainer Motion Graphics Videos
Bibliographic record
Abstract
Explainer motion graphics videos that use a combination of graphical elements and movement to convey a visual message are becoming increasingly popular among amateur creators in different domains. But, to author motion graphics videos, amateurs either have to face a steep learning curve with professional design tools or struggle with re-purposing slide-sharing tools that are easier to access but have limited animation capabilities. To simplify the process of motion graphics authoring, we present the design and implementation of Katika, an end-to-end system for creating shots based on a script, adding artworks and animation from a crowdsourced library, and editing the video using semi-automated transitions. Our observational study illustrates that participants (N=11) enjoyed using Katika and, within a one-hour session, managed to create an explainer motion graphics video. We identify opportunities for future HCI research to lower the barriers to entry and democratize the authoring of motion graphics videos.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.017 |
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".