A Confidence‐Based Multibody Kinematics Optimization for Markerless Motion Capture: A Proof of Concept
Bibliographic record
Abstract
Multi-camera markerless motion capture commonly triangulates 3D points from 2D keypoint positions in multiple camera views, then applies a multibody kinematics optimization (MKO) to incorporate biomechanical constraints. However, standard pipelines neglect the 2D confidence heatmaps generated by human pose estimation networks. We hypothesized that performing MKO in 2D camera planes would make it more robust to missing keypoints and allow us to obtain better accuracy. 2D confidence heatmaps were used to maximize available information. To test this, we first model each network-derived heatmap as a 2D Gaussian function characterized by its center, amplitude, and standard deviation. Second, we maximize the sum of these modeled confidences after projecting the biomechanical model into the camera planes. To demonstrate feasibility, we evaluated our method on data from two participants performing sit-to-stand, walking, and manual material handling, captured by a two-camera setup, and simultaneously collected marker-based data. Our Gaussian modeling of the heatmaps demonstrated a mean absolute difference of 0.011 compared to the original discrete maps, confirming its validity. In terms of 3D joint positions and angles, the confidence-based MKO produced results similar to classical distance-based methods. Notably, the confidence-based approach overcame occultations: 89.3% of frames could only be obtained with the distance-based MKO due to missing keypoints, while the confidence-based MKO computed 100% of frames. These findings underscore the potential of using full 2D confidence heatmaps in markerless motion capture, especially under challenging conditions such as sparse camera setups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".