Bibliographic record
Abstract
This paper presents a new approach to facilitate reuse and remix-ing in character animation. It demonstrates a method for automati-cally adapting existing skeletons to different characters. While the method can be applied to simple skeletons, it also proposes a new approach that is applicable to high quality animation as it is able to deal with complex skeletons that include control bones (those that drive deforming bones). Given a character mesh and a skele-ton, the method adapts the skeleton to the character by matching topology graphs between the two. It proposes specific multireso-lution and symmetry approaches as well as a simple yet effective shape descriptor. Together, these provide a robust retargeting that can also be tuned between the original skeleton shape and the mesh shape with intuitive weights. Furthermore, the method can be used for partial retargeting to directly attach skeleton parts to specific limbs. Finally, it is efficient as our prototype implementation gen-erally takes less than 30 seconds to adapt a skeleton to a character. Index Terms: I.3.7 [Computer Graphics]: Animation — [I.3.5]: Computer Graphics—Geometric algorithms, languages, and sys-tems 1
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".