首份阿拉伯手语3DAvatar重建方法，支持高精度手部与身体同步。
Tamaththul3D: High-Fidelity 3D Saudi Sign Language Avatars from Monocular Video
- 通过前臂逆运动学对齐手与身体姿态，再用2D监督优化肩部。
- 手部误差降低32%，速度比最强基线快32倍。
- 适用于5种不同手语，无需针对特定数据集微调。
现有3D手语虚拟人重建方法仅在西方手语上开发与评估，而阿拉伯手语数据集尚无3D参数化标注，阻碍了面向阿拉伯聋人群体的可访问性应用发展。本文发布首个针对Ishara-500沙特手语数据集的SMPL-X参数化标注，支持定量评估与下游手语生成。提出Tamaththul3D重建流程，通过前臂链的几何逆运动学对齐手部与身体姿态，并采用2D监督进行肩部精细化调整。该闭式集成不依赖具体身体与手部估计器：任何SMPL-X兼容的身体估计器和MANO兼容的手部估计器均可独立替换，实证有效。Tamaththul3D相较先前方法手部误差降低最多32%，运行速度提升32倍，并在五种类型迥异的手语中实现零样本泛化，无需特定数据集适配。
原文摘要 · Abstract (English)
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.
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