Tamaththul3D: High-Fidelity 3D Saudi Sign Language Avatars from Monocular Video
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918498213560320 |
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| author | Alghamdi, Eyad Altuuaim, Sattam Ghulam, Obay Qutah, Abdulrahman Basoodan, Yousef |
| author_facet | Alghamdi, Eyad Altuuaim, Sattam Ghulam, Obay Qutah, Abdulrahman Basoodan, Yousef |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05367 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tamaththul3D: High-Fidelity 3D Saudi Sign Language Avatars from Monocular Video Alghamdi, Eyad Altuuaim, Sattam Ghulam, Obay Qutah, Abdulrahman Basoodan, Yousef Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Tamaththul3D: High-Fidelity 3D Saudi Sign Language Avatars from Monocular Video |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.05367 |