Bibliographic record
Abstract
Raw optical motion capture data often includes errors such as occluded markers, mislabeled markers, and high frequency noise or jitter. Typically these errors must be fixed by hand - an extremely time-consuming and tedious task. Due to this, there is a large demand for tools or techniques which can alleviate this burden. In this research we present a tool that sidesteps this problem, and produces joint transforms directly from raw marker data (a task commonly called "solving") in a way that is extremely robust to errors in the input data using the machine learning technique of denoising. Starting with a set of marker configurations, and a large database of skeletal motion data such as the CMU motion capture database [CMU 2013b], we synthetically reconstruct marker locations using linear blend skinning and apply a unique noise function for corrupting this marker data - randomly removing and shifting markers to dynamically produce billions of examples of poses with errors similar to those found in real motion capture data. We then train a deep denoising feed-forward neural network to learn a mapping from this corrupted marker data to the corresponding transforms of the joints. Once trained, our neural network can be used as a replacement for the solving part of the motion capture pipeline, and, as it is very robust to errors, it completely removes the need for any manual clean-up of data. Our system is accurate enough to be used in production, generally achieving precision to within a few millimeters, while additionally being extremely fast to compute with low memory requirements.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".