Confidence-Based Multibody Kinematics Optimization for Markerless Motion Capture: Evaluation on Synthetic Data
Bibliographic record
Abstract
Markerless motion capture methods open the way for lighter motion analysis setups, but efforts are still needed to improve the obtained 3D kinematics. Using videos, point estimation software generate 2D confidence heatmaps. Only the position of the pixel with maximum confidence is usually used for triangulation, which neglects other possible information in the camera plane. We present and evaluate a confidence-based multibody kinematics optimization (MKO) method, which maximizes the summed 3D confidence of the model-derived points. This summed 3D confidence is obtained in a continuous and differentiable form by combining information from 2D confidence heatmaps of the surrounding cameras that were parameterized as 2D Gaussian functions. This confidence-based MKO method was evaluated using synthetic data. Typical noise of point estimation software was added to reference data in order to generate the synthetic data. Confidence-based and classical distance-based MKO methods were applied to the synthetic data. Results (joint angles and 3D point positions) were compared to those obtained with a distance-based MKO applied to the reference data. It showed a better agreement for the confidence-based MKO method and robustness to missing data, suggesting that the confidence-based MKO method performs well.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".