# Multi View 3d Pose Al Eval

> Evaluates active learning strategies for multi-view 3D pose estimation by measuring annotation efficiency. It probes how well geometric consistency and self-training can reduce the number of required human annotations while maintaining low 3D keypoint error. Use when the user wants to benchmark on CMU Panoptic, InterHand2.6M, or asks about evaluating this task. Reports 3D Mean Key Point Error (MKPE).

- Skill: `qhjqhj00/multi-view-3d-pose-al-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/multi-view-3d-pose-al-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/multi-view-3d-pose-al-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/multi-view-3d-pose-al-eval

---


# multi-view-3d-pose-al-eval

> Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training — Qi Feng et al. (2021) (arXiv:2112.13709, 2021)

## What this evaluates

Evaluates active learning strategies for multi-view 3D pose estimation by measuring annotation efficiency. It probes how well geometric consistency and self-training can reduce the number of required human annotations while maintaining low 3D keypoint error.

## Datasets

- **CMU Panoptic** — total ?; splits: train (5008), val (891), test (771)
- **InterHand2.6M** — total ?; splits: train (12123), val (1900), test (1762)

## Metrics

- `3D Mean Key Point Error (MKPE)` **(primary)** — range: other
  - Average Euclidean distance between predicted and ground truth 3D keypoints across all joints and frames, measured in millimeters.

## Input / output format

**Input**: Multi-view 2D images per frame, processed to predict 2D heatmaps for each camera view.

**Output**: 3D keypoint coordinates per frame, obtained via RANSAC triangulation of 2D keypoint predictions.

## Scoring recipe

```python
def compute_mkpe(pred_3d, gt_3d):
    # pred_3d, gt_3d: (N_frames, N_joints, 3)
    errors = np.linalg.norm(pred_3d - gt_3d, axis=-1)
    return np.mean(errors)
```

## Common pitfalls

- Extending single-view AL strategies to multi-view by simply aggregating per-frame uncertainty fails to account for geometric inconsistency, causing selection of frames that triangulate poorly.
- Sampling pseudo-labels from the full unlabeled set during self-training causes frame re-selection, leading to negative label drift over AL iterations.

## Evidence (verbatim from paper)

> For each experiment, we conduct 3 randomized trials, and report the average and variance for the 3D Mean Key Point Error (MKPE) in millimeter (mm).

## Citation

```bibtex
@misc{feng2021rethinking,
  title={Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training},
  author={Qi Feng et al. (2021)},
  year={2021},
  note={arXiv:2112.13709}
}
```

- arXiv: 2112.13709