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Tamaththul3D: High-Fidelity 3D Saudi Sign Language Avatars from Monocular Video

arXiv 2605.05367 ↗First posted 6 May 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Existing 3D sign language avatar reconstruction methods are developed and evaluated exclusively on Western sign languages, and no 3D parametric annotations exist for any Arabic Sign Language dataset, a gap that blocks the development of avatar-based accessibility applications for the Arab Deaf community. We release the first SMPL-X parametric annotations for the Ishara-500 Saudi Sign Language dataset, enabling quantitative evaluation and downstream sign language generation for Arabic Sign Language. We introduce Tamaththul3D, a reconstruction pipeline that aligns hand and body estimates through geometric inverse kinematics on the forearm chain followed by 2D-supervised shoulder refinement. The closed-form integration is decoupled from the specific choice of body and hand estimators: any SMPL-X-compatible body estimator and any MANO-compatible hand estimator can be substituted, as we demonstrate by swapping each module independently. Tamaththul3D achieves up to 32% lower hand error than prior methods, runs 32x faster than the strongest baseline, and generalizes across five typologically distinct sign languages without dataset-specific adaptation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 43 references that are in the map. First posted on arXiv 6 May 2026.

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References · 31

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19 more
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