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.
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...
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...
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.
arXiv:2608. 09045v1 Announce Type: cross Abstract: Recent advances in sign language (SL) research have shown a trend toward unifying multiple sign language understanding (SLU) subtasks, such as isolated sign language recognition (ISLR), continuous sign language recognition (CSLR), and sign language translation (SLT), within a single framework, leading to substantial progress.
SignGPT is a unified, pose‑based framework that performs gloss‑free sign language translation (SLT) and generation (SLG) by integrating part‑aware hierarchical representations of body, hand, and facial motion into a shared language model. It uses asymmetric multi‑token prediction and progressive training for bidirectional modeling, and is evaluated on How2Sign (ASL) and Phoenix‑2014T (DGS) with benchmark comparisons, qualitative analyses, and component ablations. An exploratory study with 12 Deaf ASL signers demonstrates a sign‑to‑sign response pipeline, suggesting that unified modeling can support sign language conversation (SLC).
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.
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.
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.
arXiv:2412.00112v5 Announce Type: replace Abstract: Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics a...
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.
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.
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.