Synthetic Training Image Dataset for Vision-Based 3D Pose Estimation of Construction Workers
Why this work is in the frame
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Bibliographic record
Abstract
Vision-based 3D pose estimation of construction workers has drawn attention for its usefulness in occupational ergonomics, safety, and productivity analysis. However, it is still challenging to develop an extensive training image dataset, which is essential for deep neural network-powered approaches, thus inhibiting the maximum potential of vision-based 3D pose estimation. To address this issue, we built a synthetic training image dataset and validated its effectiveness for 3D pose estimation. We trained and tested a state-of-the-art 3D pose estimation architecture using these synthetic images. The results show that the synthetic data-trained model can estimate 3D poses of construction workers with a Mean Per-Joint Position Error of 50.24 mm—comparable to real-data-trained model (46.5 mm). This finding indicates that synthesized construction images are effective in training a 3D pose estimation model, thus enabling the development of more accurate and scalable 3D pose estimation and alleviating the shortage of real-world construction training 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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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