In this work, we focus on photorealistic sign avatar modeling, which is crucial for effective communication with the Deaf community and is characterized by complex hand gestures and nuanced facial expressions. To this end, we introduce MVSign, the first multi-view Chinese sign language dataset co-designed with Deaf experts, featuring diverse gestures and rich annotations.

For precise SMPL-X annotation, we develop a hybrid fitting pipeline that produces accurate body, hand, and facial parameters and can also be applied to the monocular setting. Building on MVSign, we propose a decoupled sign avatar representation that isolates body, head, and hand components to capture complex articulations, together with a motion-aware sampling strategy to handle motion blur and balance gesture diversity.

Extensive experiments demonstrate that our method achieves high-fidelity visual results on MVSign, particularly in detailed hand and facial regions, and generalizes well to in-the-wild monocular sign language videos. Project page: https://naaapi.github.io/PHOSA.