Learning a correlated model of identity and pose-dependent body shape variation for real-time synthesis
Why this work is in the frame
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Bibliographic record
Abstract
We present a method for learning a model of human body shape variation from a corpus of 3D range scans. Our model is the first to capture both identity-dependent and pose-dependent shape variation in a correlated fashion, enabling creation of a variety of virtual human characters with realistic and non-linear body deformations that are customized to the individual. Our learning method is robust to irregular sampling in pose-space and identityspace, and also to missing surface data in the examples. Our synthesized character models are based on standard skinning techniques and can be rendered in real time. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Curve, surface, solid and object modeling; I.3.7 [Computer Graphics]: Animation 1.
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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 | 0.000 |
| 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