Comparing GPLVM approaches for dimensionality reduction in character animation
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
Gaussian Process Latent Variable Models (GPLVMs) have been found to allow dramatic dimensionality\nreduction in character animations, often yielding two-dimensional or three-dimensional spaces from which the\nanimation can be retrieved without perceptible alterations. Recently, many researchers have used this approach\nand improved on it for their purposes, thus creating a number of GPLVM-based approaches. The current paper\nintroduces the main concepts behind GPLVMs and introduces its most widely known variants. Each approach is\nthen compared based on various criteria pertaining to the task of dimensionality reduction in character\nanimation. In the light of our experiments, no single approach is preferred over all others in all respects.\nDepending whether dimensionality reduction is used for compression purposes, to interpolate new natural\nlooking poses or to synthesize entirely new motions, different approaches will be preferred.
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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 | 0.006 |
| 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