PHOSA introduces MVSign, the first multi‑view Chinese sign language dataset co‑designed with Deaf experts, featuring diverse gestures and rich annotations. The authors develop a hybrid fitting pipeline for accurate SMPL‑X annotation and propose a decoupled sign avatar representation that isolates body, head, and hand components, coupled with a motion‑aware sampling strategy to handle motion blur and balance gesture diversity. Experiments show high‑fidelity visual results on MVSign, especially in detailed hand and facial regions, and good generalization to in‑the‑wild monocular sign language videos.
By Haodong Wang, Hezhen Hu, Wengang Zhou, Houqiang Li
PHOSA presents a photorealistic 3D sign avatar modeling framework that addresses the need for accurate communication with the Deaf community. The authors introduce MVSign, a multi‑view Chinese sign language dataset co‑designed with Deaf experts, and a hybrid fitting pipeline for precise SMPL‑X annotation. Their decoupled avatar representation and motion‑aware sampling achieve high‑fidelity visual results on MVSign and generalize to monocular sign language videos.
arXiv:2605. 01720v3 Announce Type: replace-cross Abstract: Existing large-scale sign language resources typically provide supervision only at the level of raw video-text alignment and are often produced in laboratory settings.
By Sen Fang, Hongbin Zhong, Yanxin Zhang, Dimitris N. Metaxas
arXiv:2609.14122v1 Announce Type: new
Abstract: We study the challenge of sign language video mimicking: given a driving video and a single reference frame, synthesize a video where the target signer...
By Zhewen He (New York University Abu Dhabi), Junyi Yu (New York University Abu Dhabi), Haomian Huang (New York University Abu Dhabi), Zhenhua Li (ChatSign Technology), Yi Fang (New York University Abu Dhabi, ChatSign Technology)
arXiv:2610.00881v1 Announce Type: new
Abstract: Sign language machine translation has progressed substantially over the past decade, evolving from isolated sign recognition to end-to-end translation...
By Ozge Mercanoglu Sincan, Anton Pelykh, Edward Fish, Harry Walsh, JianHe Low, Karahan Sahin, Oline Ranum, Sobhan Asasi, Steven Emery, Richard Bowden
M3T introduces a discrete multi‑modal motion token system for sign language production, addressing the need for non‑manual features such as mouthings, eyebrow raises, gaze, and head movements. The approach couples FLAME’s expressive facial space with SMPL‑X body parameters and uses modality‑specific Finite Scalar Quantization VAEs to achieve high face codebook utilization (99.0%). Trained with an autoregressive transformer and a sign‑to‑text translation objective, M3T outperforms existing methods on three standard datasets, notably improving accuracy on NMFs‑CSL from 49.0% to 58.3% without large‑scale pre‑training.
By Alexandre Symeonidis-Herzig, Jianhe Low, Ozge Mercanoglu Sincan, Richard Bowden