BackTranslation2.0 -- A Linguistically Motivated Metric to Assess Sign Language Production

Sign Languages (SLs) are the primary means of communication for millions of deaf individuals, yet existing evaluation metrics for generated SL remain simplistic and poorly aligned with human judgements. We introduce BackTranslation2.0, a linguistically grounded evaluation metric for text-to-sign translation that moves beyond naïve backtranslation. Our approach adopts an agentic framework in which a deterministic pipeline orchestrates a suite of specialised tools to assess four scoring dimensions - grammatical correctness, phonological accuracy, motion fluency, and generation fidelity - aligned with human rater assessments. Tool outputs are not treated independently: a set of large language model (LLM)-based cross-referential comparison modules evaluates consistency across tools and checks outputs against linguistic expectations, enabling structured reasoning over grammatical, phonological, and motion-level evidence. Final dimension scores are computed through deterministic weighted formulas over validated tool outputs. To validate BackTranslation2.0, we introduce and evaluate on a British Sign Language (BSL) dataset rated in a human rater study across the same quality dimensions, following a protocol developed in collaboration between linguists and deaf experts, benchmarking against six baseline metrics. Our method demonstrates strong correlation with human judgements across all dimensions, providing a more comprehensive, interpretable, and linguistically principled evaluation framework for sign language production systems.

SIGNET: Motion-Level Knowledge Transfer for Cross-Language Sign Language Translation

Sign language translation (SLT) remains challenging due to its high spatio-temporal complexity, long sequences, and the need to model multiple articulators without relying on gloss annotations. Existing approaches are typically tailored to individual datasets or languages and struggle to scale, while overlooking the relationships between sign languages that could inform more effective cross-lingual transfer. We present \textbf{SIGNET}, a framework that enables motion-level knowledge transfer for cross-language sign language translation. Our key insight is that, although sign languages differ in grammar and lexicon, pretrained models capture motion-level visual patterns that can be reused across datasets and languages. \textbf{SIGNET} integrates multiple pretrained sign language backbones through an attention-based, hand-prior aggregation mechanism that guides a gated fusion network in dynamically selecting the most relevant experts. Comprehensive experiments on four benchmarks (How2Sign, Phoenix14T, CSL-Daily, and MeineDGS) demonstrate state-of-the-art translation performance, and \textbf{SIGNET} also surpasses prior methods on WLASL for sign language recognition.

What's the Point? Spatial Grammar & Index Resolution for Sign Language Processing

Sign language models are predominantly trained with gloss-sequence or text supervision, thereby under-modeling non-lexical and productive constructions. One comparatively tractable instance is spatial indexing: pointing gestures that assign discourse entities to spatial loci for subsequent co-reference, which lexicon-centric objectives largely fail to capture. We present a targeted evaluation of indexing in Sign Language Recognition, showing that despite comprising 10-15% of signing content, indexing is poorly recovered. We introduce a framework for training and evaluating indexing experts, establishing a baseline for index-aware sign language modeling. Our approach decomposes spatial reference resolution into index detection and discourse entity linking. The resulting mention representations enable automatic annotation and non-lexical structure modeling, and serve as an auxiliary indexing expert that augments a frozen SLR model at inference time.

M3T: Discrete Multi-Modal Motion Tokens for Sign Language Production

Sign language production requires more than hand motion generation. Non-manual features, including mouthings, eyebrow raises, gaze, and head movements, are grammatically obligatory and cannot be recovered from manual articulators alone. Existing 3D production systems face two barriers to integrating them: the standard body model provides a facial space too low-dimensional to encode these articulations, and when richer representations are adopted, standard discrete tokenization suffers from codebook collapse, leaving most of the expression space unreachable. We propose SMPL-FX, which couples FLAME's rich expression space with the SMPL-X body, and tokenize the resulting representation with modality-specific Finite Scalar Quantization VAEs for body, hands, and face. M3T is an autoregressive transformer trained on this multi-modal motion vocabulary, with an auxiliary translation objective that encourages semantically grounded embeddings. Across three standard benchmarks (How2Sign, CSL-Daily, Phoenix14T) M3T achieves state-of-the-art sign language production quality, and on NMFs-CSL, where signs are distinguishable only by non-manual features, reaches 58.3% accuracy against 49.0% for the strongest comparable pose baseline.

SignAgent: Agentic LLMs for Linguistically-Grounded Sign Language Annotation and Dataset Curation

This paper introduces SignAgent, a novel agentic framework that utilises Large Language Models (LLMs) for scalable, linguistically-grounded Sign Language (SL) annotation and dataset curation. Traditional computational methods for SLs often operate at the gloss level, overlooking crucial linguistic nuances, while manual linguistic annotation remains a significant bottleneck, proving too slow and expensive for the creation of large-scale, phonologically-aware datasets. SignAgent addresses these challenges through SignAgent Orchestrator, a reasoning LLM that coordinates a suite of linguistic tools, and SignGraph, a knowledge-grounded LLM that provides lexical and linguistic grounding. We evaluate our framework on two downstream annotation tasks. First, on Pseudo-gloss Annotation, where the agent performs constrained assignment, using multi-modal evidence to extract and order suitable gloss labels for signed sequences. Second, on ID Glossing, where the agent detects and refines visual clusters by reasoning over both visual similarity and phonological overlap to correctly identify and group lexical sign variants. Our results demonstrate that our agentic approach achieves strong performance for large-scale, linguistically-aware data annotation and curation.

SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning

Generating natural and linguistically accurate sign language avatars remains a formidable challenge. Current Sign Language Production (SLP) frameworks face a stark trade-off: direct text-to-pose models suffer from regression-to-the-mean effects, while dictionary-retrieval methods produce robotic, disjointed transitions. To resolve this, we propose a novel training paradigm that leverages sparse keyframes to capture the true underlying kinematic distribution of human signing. By predicting dense motion from these discrete anchors, our approach mitigates regression-to-the-mean while ensuring fluid articulation. To realize this paradigm at scale, we first introduce FAST, an ultra-efficient sign segmentation model that automatically mines precise temporal boundaries. We then present SignSparK, a large-scale Conditional Flow Matching (CFM) framework that utilizes these extracted anchors to synthesize 3D signing sequences in SMPL-X and MANO spaces. This keyframe-driven formulation also uniquely unlocks Keyframe-to-Pose (KF2P) generation, making precise spatiotemporal editing of signing sequences possible. Furthermore, our adopted reconstruction-based CFM objective also enables high-fidelity synthesis in fewer than ten sampling steps; this allows SignSparK to scale across four distinct sign languages, establishing the largest multilingual SLP framework to date. Finally, by integrating 3D Gaussian Splatting for photorealistic rendering, we demonstrate through extensive evaluation that SignSparK establishes a new state-of-the-art across diverse SLP tasks and multilingual benchmarks.

SLRTP2025 Sign Language Production Challenge: Methodology, Results, and Future Work

Sign Language Production (SLP) is the task of generating sign language video from spoken language inputs. The field has seen a range of innovations over the last few years, with the introduction of deep learning-based approaches providing significant improvements in the realism and naturalness of generated outputs. However, the lack of standardized evaluation metrics for SLP approaches hampers meaningful comparisons across different systems. To address this, we introduce the first Sign Language Production Challenge, held as part of the third SLRTP Workshop at CVPR 2025. The competition's aims are to evaluate architectures that translate from spoken language sentences to a sequence of skeleton poses, known as Text-to-Pose (T2P) translation, over a range of metrics. For our evaluation data, we use the RWTH-PHOENIX-Weather-2014T dataset, a German Sign Language - Deutsche Gebardensprache (DGS) weather broadcast dataset. In addition, we curate a custom hidden test set from a similar domain of discourse. This paper presents the challenge design and the winning methodologies. The challenge attracted 33 participants who submitted 231 solutions, with the top-performing team achieving BLEU-1 scores of 31.40 and DTW-MJE of 0.0574. The winning approach utilized a retrieval-based framework and a pre-trained language model. As part of the workshop, we release a standardized evaluation network, including high-quality skeleton extraction-based keypoints establishing a consistent baseline for the SLP field, which will enable future researchers to compare their work against a broader range of methods.

Beyond Gloss: A Hand-Centric Framework for Gloss-Free Sign Language Translation

Sign Language Translation (SLT) is a challenging task that requires bridging the modality gap between visual and linguistic information while capturing subtle variations in hand shapes and movements. To address these challenges, we introduce \textbf{BeyondGloss}, a novel gloss-free SLT framework that leverages the spatio-temporal reasoning capabilities of Video Large Language Models (VideoLLMs). Since existing VideoLLMs struggle to model long videos in detail, we propose a novel approach to generate fine-grained, temporally-aware textual descriptions of hand motion. A contrastive alignment module aligns these descriptions with video features during pre-training, encouraging the model to focus on hand-centric temporal dynamics and distinguish signs more effectively. To further enrich hand-specific representations, we distill fine-grained features from HaMeR. Additionally, we apply a contrastive loss between sign video representations and target language embeddings to reduce the modality gap in pre-training. \textbf{BeyondGloss} achieves state-of-the-art performance on the Phoenix14T and CSL-Daily benchmarks, demonstrating the effectiveness of the proposed framework. We will release the code upon acceptance of the paper.

Hierarchical Feature Alignment for Gloss-Free Sign Language Translation

Sign Language Translation (SLT) attempts to convert sign language videos into spoken sentences. However, many existing methods struggle with the disparity between visual and textual representations during end-to-end learning. Gloss-based approaches help to bridge this gap by leveraging structured linguistic information. While, gloss-free methods offer greater flexibility and remove the burden of annotation, they require effective alignment strategies. Recent advances in Large Language Models (LLMs) have enabled gloss-free SLT by generating text-like representations from sign videos. In this work, we introduce a novel hierarchical pre-training strategy inspired by the structure of sign language, incorporating pseudo-glosses and contrastive video-language alignment. Our method hierarchically extracts features at frame, segment, and video levels, aligning them with pseudo-glosses and the spoken sentence to enhance translation quality. Experiments demonstrate that our approach improves BLEU-4 and ROUGE scores while maintaining efficiency.

VisualSpeaker: Visually-Guided 3D Avatar Lip Synthesis

Realistic, high-fidelity 3D facial animations are crucial for expressive avatar systems in human-computer interaction and accessibility. Although prior methods show promising quality, their reliance on the mesh domain limits their ability to fully leverage the rapid visual innovations seen in 2D computer vision and graphics. We propose VisualSpeaker, a novel method that bridges this gap using photorealistic differentiable rendering, supervised by visual speech recognition, for improved 3D facial animation. Our contribution is a perceptual lip-reading loss, derived by passing photorealistic 3D Gaussian Splatting avatar renders through a pre-trained Visual Automatic Speech Recognition model during training. Evaluation on the MEAD dataset demonstrates that VisualSpeaker improves both the standard Lip Vertex Error metric by 56.1% and the perceptual quality of the generated animations, while retaining the controllability of mesh-driven animation. This perceptual focus naturally supports accurate mouthings, essential cues that disambiguate similar manual signs in sign language avatars.