arXiv Computation and Language

Investigating Temporal Motion Features for Pose-to-Text Indian Sign Language Translation

arXiv AI
Jul 7

ViPo-MLLM: Visual-Pose Multimodal LLM for Gloss-Free Sign Language Translation

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 Computer Vision
3d ago

SignMimic: Robust High-Quality Sign Language Motion Generation via Human-Shape-Oblivious Pose Transfer Guidance

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 Computer Vision
Aug 27

SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting

SMART is a new framework that jointly tackles continuous sign language recognition (CSLR) and spotting by leveraging a multimodal large language model (MLLM) to generate motion descriptions as auxiliary semantic cues. It performs stable video‑text alignment with small batch sizes and introduces a Multi‑Scale Temporal Adapter to capture temporal interactions during transformer encoding. The framework also incorporates CSFormer, a CSLR‑guided spotting module that injects recognition‑derived gloss evidence into a boundary‑aware spotting network, enabling mutual benefit between recognition and spotting tasks.

By Eunjee Choi, JungHoon Sung, Seongwhan Cho, Chu Xin, Younggeun Choi
arXiv AI
Sep 4

Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks

The paper argues that BLEU-4, the prevailing metric for sign language translation (SLT), may not accurately reflect sign language proficiency because SLT models can exploit spurious correlations and spoken-language priors. By evaluating six SLT models on Phoenix-2014T and CSL-Daily, the authors show that higher BLEU-4 scores do not necessarily indicate better spatio-temporal understanding. They propose a new open-weight LLM QA protocol inspired by language-learning assessment, which better preserves salient content, aligns more closely with human rankings, and reveals differences between gloss-free and gloss-supervised systems that BLEU-4 obscures.

By Oline Ranum, Edward Fish, Simon Hadfield, Richard Bowden
arXiv Machine Learning
Jun 11

Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching

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
arXiv AI
Jul 14

Emotion Recognition in Signers

arXiv:2512. 15376v2 Announce Type: replace-cross Abstract: Recognition of signers' emotions suffers from one theoretical challenge and one practical challenge, namely, the overlap between grammatical and affective facial expressions and the scarcity of data for model training.

By Kotaro Funakoshi, Yaoxiong Zhu
arXiv Computer Vision
Aug 31

SignRR: Retrieve and Refine Real Motion for Sign Language Production

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