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Ham2Pose: Animating Sign Language Notation Into Pose Sequences
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use normalized DTW-MJE with missing-keypoint handling instead of existing pose distances | Pose-sequence similarity measurement · AUTSL | mAP: 0.44 -> 0.58 | Helped |
| Use normalized keypoints for pose distance measurements | MSE, APE, and DTW-MJE measurements · AUTSL | mAP: MSE: 0.27 -> 0.4; APE: 0.29 -> 0.44; DTW-MJE: 0.33 -> 0.58 | Helped |
| Generate poses with Ham2Pose rather than Progressive Transformers | HamNoSys-to-pose generation · HamNoSys-to-pose distance ranks | Rank 1 (distance to ground truth): 0.0 -> 0.21 | Helped |
| Generate poses with Ham2Pose rather than a constant resting pose | HamNoSys-to-pose generation · HamNoSys-to-pose distance ranks | Rank 1 (distance to ground truth): 0.008 -> 0.21 | Helped |
| Train without one Sign language and test on that held-out language (ablation) | Ham2Pose · PJM, DGS, GSL, and LSF leave-one-out | Distance ranks | Mixed |
Closest papers by meaning
- G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model (AAAI 2024 · similarity 0.89)
- Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment (NeurIPS 2026 · similarity 0.89)
- Scaling Sign Language Translation (NeurIPS 2024 · similarity 0.88)
- VSNet: Focusing on the Linguistic Characteristics of Sign Language (CVPR 2025 · similarity 0.88)
- Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation (NeurIPS 2025 · similarity 0.88)
- Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation (ICLR 2024 · similarity 0.88)
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