SignRR: Retrieve and Refine Real Motion for Sign Language Production

• URL: http://arxiv.org/abs/2608.28568v2
• Date: Tue, 01 Sep 2026 15:13:52 GMT
• Title: SignRR: Retrieve and Refine Real Motion for Sign Language Production
• Abstract summary: We propose retrieve-and-refine, a paradigm that starts from real retrieved motion and refines it into a globally coherent signing sequence.<n>Our framework, SignRR, initializes motion from a dictionary of real sign segments and refines the full sequence with a part-aware Residual VQ-VAE.
• Score: 1.0332950685403934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sign language production (SLP) aims to generate continuous signing motion from spoken language, often through gloss-to-pose generation. Prior work mainly follows two paradigms. Generative models synthesize motion from a learned prior or from noise, without reference to an observed signing instance, making rare hand configurations and signer-specific articulation difficult to preserve. Retrieval-based methods reuse real, well-articulated motion segments, but concatenating segments from different signers and co-articulation contexts can introduce rhythm and style inconsistencies across the full sequence, not only at segment boundaries. These limitations suggest a complementary solution: use retrieval to provide realistic articulation, and use learned refinement to impose the global coherence that retrieval alone lacks. We therefore propose retrieve-and-refine, a paradigm that starts from real retrieved motion and refines it into a globally coherent signing sequence rather than generating motion from scratch. Our framework, SignRR, initializes motion from a dictionary of real sign segments and refines the full sequence with a part-aware Residual VQ-VAE, where residual quantization preserves fine hand articulation and temporal length differences are handled in the latent space. Experiments on PHOENIX14T and CSL-Daily show that SignRR achieves state-of-the-art back-translation performance while maintaining competitive pose quality.

Related papers

• Bridging the Gap Between Semantics and Reconstruction:Unifying Sign Language Translation and Production [16.56134410439223]
  Recent advances in sign language research have shown a trend toward unifying multiple sign language understanding subtasks.<n>Can sign language understanding and production be unified within a single framework?<n>A unified framework must address two key challenges: bridging the gap between continuous sign motions and discrete text tokens.
  arXiv  Detail & Related papers  (2026-08-10T02:50:07Z)
• SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning [54.232148007248874]
  Current Sign Language Production (SLP) frameworks face a stark trade-off: direct text-to-pose models suffer from regression-to-the-mean effects.<n>We propose a novel training paradigm that leverages sparses to capture the true underlying distribution of human signing.<n>By predicting dense motion from these discrete anchors, our approach mitigates regression-to-the-mean while ensuring fluid articulation.
  arXiv  Detail & Related papers  (2026-03-11T06:02:36Z)
• MIBURI: Towards Expressive Interactive Gesture Synthesis [62.45332399212876]
  Embodied Conversational Agents (ECAs) aim to emulate human face-to-face interaction through speech, gestures, and facial expressions.<n>Existing solutions for ECAs produce rigid, low-diversity motions that are unsuitable for human-like interaction.<n>We present MIBURI, the first online, causal framework for generating expressive full-body gestures and facial expressions synchronized with real-time spoken dialogue.
  arXiv  Detail & Related papers  (2026-03-03T18:59:51Z)
• Beyond Global Alignment: Fine-Grained Motion-Language Retrieval via Pyramidal Shapley-Taylor Learning [56.6025512458557]
  Motion-language retrieval aims to bridge the semantic gap between natural language and human motion.<n>Existing approaches predominantly focus on aligning entire motion sequences with global textual representations.<n>We propose a novel Pyramidal Shapley-Taylor (PST) learning framework for fine-grained motion-language retrieval.
  arXiv  Detail & Related papers  (2026-01-29T16:00:12Z)
• AutoSign: Direct Pose-to-Text Translation for Continuous Sign Language Recognition [0.0]
  Continuously recognizing sign gestures and converting them to glosses plays a key role in bridging the gap between hearing and hearing-impaired communities.<n>We propose AutoSign, an autoregressive decoder-only transformer that directly translates pose sequences to natural language text.<n>By eliminating the multi-stage pipeline, AutoSign achieves substantial improvements on the Isharah-1000 dataset.
  arXiv  Detail & Related papers  (2025-07-26T07:28:33Z)
• MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production [93.32354378820648]
  We propose a unified framework for continuous sign language production, easing communication between sign and non-sign language users. A sequence diffusion model, utilizing embeddings extracted from text or speech, is crafted to generate sign predictions step by step. Experiments on How2Sign and PHOENIX14T datasets demonstrate that our model achieves competitive performance in sign language production.
  arXiv  Detail & Related papers  (2024-07-04T13:53:50Z)
• MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition [94.56755080185732]
  We propose a Motion-Aware masked autoencoder with Semantic Alignment (MASA) that integrates rich motion cues and global semantic information. Our framework can simultaneously learn local motion cues and global semantic features for comprehensive sign language representation.
  arXiv  Detail & Related papers  (2024-05-31T08:06:05Z)
• Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production [43.45785951443149]
  Sign languages are visual languages, with vocabularies as rich as their spoken language counterparts. Current deep-learning based Sign Language Production (SLP) models produce under-articulated skeleton pose sequences. We tackle large-scale SLP by learning to co-articulate between dictionary signs. We also propose SignGAN, a pose-conditioned human synthesis model that produces photo-realistic sign language videos.
  arXiv  Detail & Related papers  (2022-03-29T08:51:38Z)
• Pose-based Sign Language Recognition using GCN and BERT [0.0]
  Word-level sign language recognition (WSLR) is the first important step towards understanding and interpreting sign language. recognizing signs from videos is a challenging task as the meaning of a word depends on a combination of subtle body motions, hand configurations, and other movements. Recent pose-based architectures for W SLR either model both the spatial and temporal dependencies among the poses in different frames simultaneously or only model the temporal information without fully utilizing the spatial information. We tackle the problem of W SLR using a novel pose-based approach, which captures spatial and temporal information separately and performs late fusion.
  arXiv  Detail & Related papers  (2020-12-01T19:10:50Z)
• Video-based Sign Language Recognition without Temporal Segmentation [88.03159640595187]
  We propose a novel continuous sign recognition framework, which eliminates the preprocessing of temporal segmentation.<n>The proposed LS-HAN consists of three components: a two-stream Convolutional Neural Network (CNN) for video feature representation generation, a Latent Space for semantic gap bridging, and a Hierarchical Attention Network (HAN) for latent space based recognition.
  arXiv  Detail & Related papers  (2018-01-30T17:37:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.