Tracking human joint motion for turntable-based static model reconstruction
Bibliographic record
Abstract
We propose a method that makes standard turntable-based vision acquisition a practical method for recovering models of human geometry. A human subject typically exhibits some unintended joint motion while rotating on a turntable. Ignoring such motion causes shape-from-silhouette to excessively carve the model, resulting in loss of geometry (especially on limbs). We utilize silhouette cues with an initial automatically recovered skinned-model to recover this joint motion, or wobbling. The recovered joint motion gives the calibration of each rigid body of the subject, allowing for temporal fusion of image cues (e.g., silhouettes and texture) used to refine the geometry. Our method gives improved results on real data sets when considering silhouette overlap in novel views. The recovered geometry is useful in vision tasks such as multi-view image-based tracking of humans, where the recent trend of using a priori laser-scanned geometry could be replaced with a more cost effective vision-based geometry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".