Continuous sign language recognition (CSLR) aims to recognize gloss sequences from unsegmented sign videos under weak sequence-level supervision. However, existing methods rely on sentence-level gloss annotations, providing limited temporal and semantic guidance for fine-grained representation learning. Conventional video-text alignment also requires large batch sizes, making it inefficient for memory-intensive sign language video training. In this work, we propose SMART, an MLLM-guided temporal alignment framework for joint sign recognition and spotting. SMART uses MLLMgenerated motion descriptions as auxiliary semantic cues and performs stable videotext alignment under small-batch training. To improve temporal representation learning, we introduce a Multi-Scale Temporal Adapter that models temporal interactions during transformer encoding. For dense temporal localization, SMART incorporates CSFormer, a CSLR-guided spotting module that injects recognition-derived gloss evidence into a boundary-aware spotting network. This unified framework enables CSLR features to benefit spotting, while spotting supervision complements weak CTC-based recognition. Experiments on four sign language benchmarks, including PHOENIX14-T, CSL-Daily, Large-scale KSL, and Disaster and Safety KSL datasets, demonstrate the effectiveness of SMART across both recognition and spotting tasks.
Automated Sign Language Recognition for under-represented languages remains a largely unsolved problem. Central African Sign Language (CASL) exemplifies this gap: the only available bench-mark, CASL-W60, has a best reported accuracy of 69.93%, and we show that the common heuristic of fine-tuning high-resource models fails to close it. This failure stems from two compounding factors: the limited scale of available CASL data and the significant lexical and visual domain gap between CASL and large-scale corpora such as WLASL, which renders pre-trained representations largely uninformative. To address this, we propose TransSLR, a lightweight Temporal Transformer Encoder trained from scratch on 64-frame normalized pose sequences, with average pooling and a classification head. By operating on geometric keypoint representations rather than raw RGB, TransSLR achieves signer-independent generalization without relying on visual appearance. On the CASL-W60 benchmark, TransSLR establishes a new state-of-the-art accuracy of 80.39%, surpassing the prior best by +10.46%. Beyond accuracy, our encoder-only design significantly reduces computational overhead, making deployment feasible in resource-constrained environments. We conduct extensive experiments on the CASL-W60 benchmark, comparing against RGB-based and multimodal baselines, and demonstrate that TransSLR achieves state-of-the-art performance.
Lucia Yen Wanchi, Samuel Johnny, Victor Tolulope Olufemi +2
Sign language generation (SLG), also known as text-to-sign generation, aims to bridge the communication gap between signers and non-signers. Unlike many other generative tasks, SLG must satisfy two fundamental linguistic constraints. First, sign language expresses meaning through a sequence of gestures aligned with word-like units called glosses, and therefore requires correct lexical ordering to preserve intended meaning. Second, each gesture should faithfully reflect the intended gloss (semantic accuracy). Despite recent progress, existing SLG methods frequently produce signs with incorrect lexical order and low semantic accuracy. A common limitation of prior approaches stems from globally fused conditioning strategies, which weaken temporal grounding, the temporal correspondence between glosses and their realized sign segments. This often leads to incorrect lexical order and semantically ambiguous signs. To address this limitation, we propose SIGNER, a SIGN language generation framework with timE-Resolved conditioning to ensure temporal grounding, leveraging a temporal-gloss condition and local temporal fusion (LTF). SIGNER constructs a temporal-gloss condition by estimating a gloss sequence and its durations from input text, and assigning gloss semantics across the temporal dimension. We then introduce LTF, a temporally grounded fusion module that integrates the temporal-gloss condition within a constrained temporal window during denoising. By enforcing temporal locality in condition fusion, LTF preserves temporal grounding, leading to correct lexical ordering and clearer per-gloss semantics. Experiments on Phoenix-2014T and CSL-Daily demonstrate state-of-the-art performance, further supported by motion-smoothness analysis. The project page is available here https://taeryunglee.github.io/projects/signer.
Sign Language Production (SLP) faces a fundamental trade-off: direct text-to-pose models suffer from regression-to-the-mean effects, while dictionary-retrieval methods produce disjointed transitions. To resolve this, we propose a novel training paradigm that leverages sparse keyframes to capture the underlying kinematic distribution of human signing. By generating dense motion from discrete anchors, our approach mitigates regression-to-the-mean while ensuring fluid articulation. To achieve this at scale, we introduce FAST, an ultra-efficient sign segmentation model that automatically mines precise temporal boundaries. We then present SignSparK, a Conditional Flow Matching (CFM) framework that utilizes these temporal anchors to synthesize 3D signing sequences. This keyframe-driven formulation also unlocks Keyframe-to-Pose (KF2P) generation, making precise spatiotemporal editing of signing sequences possible. Furthermore, SignSparK scales across four distinct sign languages, constituting the largest multilingual SLP framework to date, and integrates 3D Gaussian Splatting for photorealistic rendering. Extensive evaluations demonstrate that SignSparK achieves state-of-the-art across diverse SLP tasks and multilingual benchmarks. Our code is available at https://github.com/JianHe0628/SignSparK.