Recurrent 3D Hand Pose Estimation Using Cascaded Pose-Guided 3D Alignments
Bibliographic record
Abstract
3D hand pose estimation is a challenging problem in computer vision due to the high degrees-of-freedom of hand articulated motion space and large viewpoint variation. As a consequence, similar poses observed from multiple views can be dramatically different. In order to deal with this issue, view-independent features are required to achieve state-of-the-art performance. In this paper, we investigate the impact of view-independent features on 3D hand pose estimation from a single depth image, and propose a novel recurrent neural network for 3D hand pose estimation, in which a cascaded 3D pose-guided alignment strategy is designed for view-independent feature extraction and a recurrent hand pose module is designed for modeling the dependencies among sequential aligned features for 3D hand pose estimation. In particular, our cascaded pose-guided 3D alignments are performed in 3D space in a coarse-to-fine fashion. First, hand joints are predicted and globally transformed into a canonical reference frame; Second, the palm of the hand is detected and aligned; Third, local transformations are applied to the fingers to refine the final predictions. The proposed recurrent hand pose module for aligned 3D representation can extract recurrent pose-aware features and iteratively refines the estimated hand pose. Our recurrent module could be utilized for both single-view estimation and sequence-based estimation with 3D hand pose tracking. Experiments show that our method improves the state-of-the-art by a large margin on popular benchmarks with the simple yet efficient alignment and network architectures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".