Dynamic Comics for Hierarchical Abstraction of 3D Animation Data
Bibliographic record
Abstract
Abstract Image storyboards of films and videos are useful for quick browsing and automatic video processing. A common approach for producing image storyboards is to display a set of selected key‐frames in temporal order, which has been widely used for 2D video data. However, such an approach cannot be applied for 3D animation data because different information is revealed by changing parameters such as the viewing angle and the duration of the animation. Also, the interests of the viewer may be different from person to person. As a result, it is difficult to draw a single image that perfectly abstracts the entire 3D animation data. In this paper, we propose a system that allows users to interactively browse an animation and produce a comic sequence out of it. Each snapshot in the comic optimally visualizes a duration of the original animation, taking into account the geometry and motion of the characters and objects in the scene. This is achieved by a novel algorithm that automatically produces a hierarchy of snapshots from the input animation. Our user interface allows users to arrange the snapshots according to the complexity of the movements by the characters and objects, the duration of the animation and the page area to visualize the comic sequence. Our system is useful for quickly browsing through a large amount of animation data and semi‐automatically synthesizing a storyboard from a long sequence of animation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".