[2605.05367] Tamaththul3D: High-Fidelity 3D Saudi Sign Language Avatars from Monocular Video
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.05367 (cs)
[Submitted on 6 May 2026]
Title:Tamaththul3D: High-Fidelity 3D Saudi Sign Language Avatars from Monocular Video
Authors:Eyad Alghamdi, Sattam Altuuaim, Obay Ghulam, Abdulrahman Qutah, Yousef Basoodan
View a PDF of the paper titled Tamaththul3D: High-Fidelity 3D Saudi Sign Language Avatars from Monocular Video, by Eyad Alghamdi and 4 other authors
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Abstract:Arabic Sign Language (ArSL) and its dialects serve approximately 400 million Arabic speakers worldwide, yet the community lacks high-quality 3D parametric annotations and specialized reconstruction methods for avatar generation. We address this critical gap through two key contributions: First, we introduce the first high-quality 3D parametric annotations for the Ishara-500 Saudi Sign Language dataset, providing precise SMPL-X parameters for 500 culturally authentic SSL signs. Second, we present Tamaththul3D, a specialized reconstruction pipeline designed for ArSL's unique articulation patterns. Our pipeline integrates SMPLer-X for robust body estimation, WiLoR for detailed hand refinement with automatic localization and mirroring, and MediaPipe for 2D pose supervision. Through kinematic-chain-based wrist alignment with hybrid swing-twist decomposition and 2D-supervised joint optimization, Tamaththul3D achieves state-of-the-art hand accuracy (up to 32% improvement over previous methods) while maintaining competitive body pose. Together, these 3D annotations and Tamaththul3D pipeline establish the first comprehensive framework for high-fidelity ArSL avatar reconstruction, enabling new accessibility technologies and cultural preservation efforts for the Arab Deaf community.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.05367 [cs.CV]
(or arXiv:2605.05367v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.05367
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arXiv-issued DOI via DataCite
Submission history
From: Eyad Alghamdi [view email] [v1] Wed, 6 May 2026 18:47:49 UTC (48,490 KB)
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