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.
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.
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.
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.
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.
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.
InteractGesture is a model‑agnostic, inference‑time method that enables fine‑grained spatial control of individual joints in continuous streaming co‑speech gesture generation. It guides diffusion sampler latent estimates through a differentiable RVQ‑VAE decoder, backpropagating spatial control gradients to adjust motion latents during sampling. To address chunk‑wise dependency issues in streaming generation, the method introduces Progressive Chunk Guidance, a chunk‑window strategy that keeps an active set of editable chunk latents with staggered delays, allowing spatial constraints to propagate gradients backward across chunk boundaries and reducing boundary inconsistencies.
arXiv:2204. 02803v2 Announce Type: replace-cross Abstract: Sign language recognition from monocular video or 2D pose sequences is challenging, both because 3D information must be inferred from 2D observations and because the signal is inherently spatiotemporal.
STyMo is a few‑shot motion style transfer method that learns from only seconds of paired data and trains in one to two minutes. It decomposes style into a static posture component and a temporal dynamics component, allowing runtime adjustment of posture intensity, temporal exaggeration, and per‑body‑region style. The approach includes a stylizability gate to avoid artifacts on out‑of‑distribution motions and supports an iterative authoring workflow, with results shown across a range of motion styles and a released dataset for future research.