Multi-constrained inverse kinematics for the human hand
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Bibliographic record
Abstract
Measuring the spatial and temporal characteristics of hand movement is a challenging task due to the large number of degrees of freedom (DOF) in the hand. This paper presents a multi-constrained inverse kinematics (IK) approach for hand motion estimation from motion capture data. The IK approach satisfies a set of prioritized motion and postural constraints for each hand joint and link. The high-priority constraint is fully satisfied, while the fulfillment of the low-priority constraints is achieved as long as no conflict with the high-priority constraint exists. The proposed approach can aid marker-based motion capture technologies in accurately reconstructing discontinuities or erroneous marker trajectory segments resulting from occluded, missing, or flipped markers. The performance of the multi-constrained IK approach for the hand is tested for a full range of continuous hand motion.
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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