Sequential Gesture Learning for Continuous Labanotation Generation Based on the Fusion of Graph Neural Networks
Bibliographic record
Abstract
Labanotation is a symbolic recording system for human movements, and also a powerful tool for protecting and spreading folk dances and other performing arts. State-of-the-art automatic Labanotation uses end-to-end methods with sequence-based skeleton representation, which cannot capture the relationship between joints and bones in the skeleton for accurate descriptions of continuous lower limb movements such as dance steps. In this paper, we propose a novel double-stream fusion method of directed graph neural networks (DGNN), combined with connectionist temporal classification (CTC), namely DFGNN-CTC, for sequential fine-grained motion recognition, such as the Labanotation generation of unsegmented dance movement. First, we extract double-stream directed graph feature, employing an orientation-normalized directed acyclic graph (ON-DAG) and an orientation-normalized temporal directed acyclic graph (ON-TDAG), to jointly model spatiotemporal properties of movement recorded in motion capture data. Then, we design a CTC-based fusion-pooling module to fuse the spatial and temporal streams encoded by two DGNNs. It concatenates and fuses the two streams to generate discriminative descriptions of each time step, and concentrates them to make per-time-step predictions of Laban gesture type, from which the CTC searches the optimal Laban symbol sequence, corresponding to elemental motions composing the movement. In this way, the new method enables much finer discrimination for similar Laban gestures with subtle differences in spatial and temporal properties through joint contextual spatiotemporal modeling so that it achieves much superior performance in continuous Labanotation generation to existing methods, which only have single-stream analysis either spatially or temporally. The experiments on two Labanotation-labelled motion capture datasets demonstrate the effectiveness of the components in the proposed method and its superiority comparing with the state-of-the-art methods, especially for lower limb movements.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".