SignRR is a new sign language production framework that combines retrieval of real sign motion segments with a learned refinement step to produce globally coherent signing sequences. It starts from a dictionary of authentic sign segments and refines them using a part-aware Residual VQ‑VAE, preserving fine hand articulation while handling temporal length differences in latent space. Experiments on PHOENIX14T and CSL‑Daily demonstrate state‑of‑the‑art back‑translation performance and competitive pose quality.
By Fidel Omar Tito Cruz, Angie Sanchez Marquina, Summy Farfan, Gissella Bejarano
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:2609.05742v1 Announce Type: cross
Abstract: American Sign Language (ASL) generation remains challenging due to limited paired text-ASL motion data and the difficulty of learning motion represen...
By Hongyu Wu, Xu Wu, Tianhao Wu, Jiawei Yu, Phuc Nguyen, Jian Liu, Yi Wu
SignDEG (DINO Eats Geometry) is a temporal sign language encoder that incorporates articulated hand geometry as a pre‑training target while using only video at inference. It leverages frozen DINOv3 features of tracked hands and faces, feeding them into per‑stream temporal Transformers, and introduces two geometric objectives—Geometric Latent Prediction (GLP) and Geometric Relation Transfer (GRT)—to supervise intermediate and final blocks. Jointly trained with temporal self‑distillation, SignDEG achieves higher BLEU‑4 scores on How2Sign/OpenASL and improves recall on ASL Citizen and Sem‑Lex datasets compared to SHuBERT, demonstrating that articulated structure can be transferred into an appearance encoder without requiring a hand reconstruction model at deployment.
By Junyi Hu, Zhewen He, Haomian Huang, Zhenhua Li, Zhifei Li, Shuaihang Yuan, Yi Fang
arXiv:2606. 11925v1 Announce Type: cross Abstract: Sign language translation (SLT) converts sign language video into spoken language text and holds significant promise for improving accessibility and enabling communication between signing and non-signing communities.
By Zsolt Robotka, \'Ad\'am R\'ak, Jalal Al-Afandi, Andr\'as Horv\'ath, Gy\"orgy Cserey
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
Accurate monocular 4D hand reconstruction remains challenging. Per-frame discriminative regressors lack temporal context and often produce jittery predictions.
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
arXiv:2607. 03657v1 Announce Type: cross Abstract: Gloss-free Sign Language Translation (SLT) translates sign language videos into spoken-language sentences without gloss annotations, avoiding costly labeling but requiring fine-grained modeling of hands, body, and facial cues.
By Ahmed Abul Hasanaath, Bicheng Xu, Mir Rayat Imtiaz Hossain, Leonid Sigal, Hamzah Luqman
arXiv:2607. 11221v1 Announce Type: cross Abstract: Accurate monocular 4D hand reconstruction remains challenging.
By Mingxi Xu, Bowen Duan, Yi Gu, Zhengyang Shen, Renjing Xu, Yutao Yue
SignMatch introduces a prototype‑structured embedding space that learns to match dictionary sign videos with continuous sign language footage based solely on visual similarity of handshape and motion. By mapping isolated dictionary exemplars into this space, the method enables direct, embedding‑based sign matching and can generalise to unseen signs using only dictionary examples. Experiments on ASL‑Citizen, ChaLearn OSLWL, and BOBSL CSLR2 benchmarks show strong cross‑dataset, cross‑task, and cross‑language performance, outperforming prior approaches on American, British, and Spanish sign languages without benchmark‑specific supervision.
By Ryan Wong, Youngjoon Jang, Liliane Momeni, G\"ul Varol, Andrew Zisserman
arXiv:2610.32517v1 Announce Type: new
Abstract: Hand landmarks serve as input for gesture recognition, sign language understanding, and touchless human-computer interaction. Yet hand pose estimators...
By Gibran Benitez-Garcia, Hiroki Takahashi