Learning Three-dimensional Skeleton Data from Sign Language Video
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data for sign language research is often difficult and costly to acquire. We therefore present a novel pipeline able to generate motion three-dimensional (3D) skeleton data from single-camera sign language videos only. First, three recurrent neural networks are learned to infer the three-dimensional position data of body, face, and finger joints for a high resolution of the signer’s skeleton. Subsequently, the angular displacements of all joints over time are estimated using inverse kinematics and mapped to a virtual sign avatar for animation. Last, the generated data are evaluated in detail, including a sign language recognition and sign language synthesis scenario. Utilizing a neural word classifier trained on real motion capture data, we reliably classify word segments built from our newly generated position data with similar accuracy as motion capture data (absolute difference 3.8%). Furthermore, qualitative evaluation of sign animations shows that the avatar performs natural movements that are comprehensible and resemble animations created with original motion capture data.
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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 | 0.001 | 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