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
A significant fraction of the world's population have experienced virtual characters through games and movies, and the possibility of online VR social experiences may greatly extend this audience. At present, the skin deformation for interactive and real-time characters is typically computed using geometric skinning methods. These methods are efficient and simple to implement, but obtaining quality results requires considerable manual "rigging" effort involving trial-and-error weight painting, the addition of virtual helper bones, etc. The recently introduced Delta Mush algorithm largely solves this rig authoring problem, but its iterative computational approach has prevented direct adoption in real-time engines. This paper introduces Direct Delta Mush, a new algorithm that simultaneously improves on the efficiency and control of Delta Mush while generalizing previous algorithms. Specifically, we derive a direct rather than iterative algorithm that has the same ballpark computational form as some previous geometric weight blending algorithms. Straightforward variants of the algorithm are then proposed to further optimize computational and storage cost with insignificant quality losses. These variants are equivalent to special cases of several previous skinning algorithms. Our algorithm simultaneously satisfies the goals of reasonable efficiency, quality, and ease of authoring. Further, its explicit decomposition of rotational and translational effects allows independent control over bending versus twisting deformation, as well as a skin sliding effect.
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.
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