Synthetic Training Image Dataset for Vision-Based 3D Pose Estimation of Construction Workers
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".