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

2026-05-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision · Depth: Expert, quick

Summary

Tamaththul3D is a novel reconstruction pipeline designed to create high-fidelity 3D Saudi Sign Language avatars from monocular video, addressing a critical gap in existing methods that primarily focus on Western sign languages. This work introduces the first SMPL-X parametric annotations for the Ishara-500 Saudi Sign Language dataset, enabling quantitative evaluation and generation for Arabic Sign Language. The Tamaththul3D pipeline aligns hand and body estimates through geometric inverse kinematics on the forearm chain, followed by 2D-supervised shoulder refinement. Its modular architecture allows for independent substitution of any SMPL-X-compatible body estimator and MANO-compatible hand estimator. Tamaththul3D demonstrates significant performance improvements, achieving up to 32% lower hand error than previous methods and running 32x faster than the strongest baseline. Furthermore, it generalizes effectively across five typologically distinct sign languages without requiring dataset-specific adaptation, paving the way for enhanced accessibility applications for the Arab Deaf community.

Key takeaway

For AI Engineers developing accessibility applications for the Arab Deaf community, Tamaththul3D offers a robust solution for generating high-fidelity 3D sign language avatars. You can utilize its modular design to integrate preferred body and hand estimators, significantly reducing hand error by up to 32% and accelerating processing 32x compared to previous methods. This advancement means your projects can achieve superior performance and broader linguistic generalization without extensive dataset-specific adaptations, directly improving avatar-based communication tools.

Key insights

Tamaththul3D enables high-fidelity 3D sign language avatar creation for Arabic Sign Language, outperforming prior methods in speed and accuracy.

Principles

• Modular design allows estimator swapping.
• Geometric inverse kinematics refines body-hand alignment.
• Dataset-specific adaptation is not required for generalization.

Method

The Tamaththul3D pipeline aligns hand and body estimates via geometric inverse kinematics on the forearm chain, then refines shoulders using 2D supervision.

In practice

• Generate 3D avatars for Arabic Sign Language accessibility.
• Integrate SMPL-X and MANO compatible estimators.
• Apply pipeline to diverse sign languages without retraining.

Topics

• 3D Avatar Generation
• Saudi Sign Language
• Monocular Video Reconstruction
• SMPL-X Parametric Models
• Inverse Kinematics
• Accessibility Applications

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.