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SignFLIP: A Unified Model for Sign Language Translation and Generation via Stage-wise Alignment at Scale

Zhaoyi An, Sihan Tan, Youngbae Hwang, Kazuhiro Nakadai, Rei Kawakami

arXiv:2609.35225v1cs.CLcs.CVcs.MM

TL;DR

Sign language translation and generation are often treated as separate tasks, and unified sign–text modeling remains underexplored. SignFLIP uses a large-scale, stage-wise framework to refine shared representations for both directions. It reports competitive translation and generation performance and transfer to sign language recognition.

  • Problem

    Sign language translation and generation are often treated as separate problems, despite their shared sign–text mapping and the potential to benefit from their inherent duality.

    Source: §1 · p. 1

  • Method

    SignFLIP learns sign–text mappings within a general LLM’s latent space using large-scale training and stage-wise refinement of shared representations.

    Source: §1 · p. 2

  • Results

    SignFLIP achieves competitive performance on SLT and SLG and learns transferable representations that benefit isolated and continuous SLR.

    Source: §1 · p. 2, §5 · p. 9

  • Takeaways & Limitations

    The results suggest SignFLIP learns generalizable sign representations with potential as a foundation model for sign language processing.

    Source: §5 · p. 9

    Most existing datasets are not primarily based on Deaf and hard-of-hearing signers, limiting their representativeness of real-world sign language use.

    Source: Limitations · p. 9

Abstract

from arXiv · show

Sign language translation and generation share the goal of bidirectional alignment between text and sign representations. However, existing approaches either treat them as isolated tasks or are only verified on limited datasets, limiting effective modeling between modalities. In this paper, we propose SignFLIP, a unified LLM-centered framework for translation and generation. To enable bidirectional mapping between text and sign, SignFLIP adopts a symmetric architecture together with a stage-wise training strategy built on large-scale data. The shared sign--text representation is progressively refined: pre-alignment facilitates subsequent SLT, while the SLT-adapted representation further benefits SLG. Extensive experiments on multiple benchmarks show that SignFLIP shows competitive performance compared with task-specific models on both translation and generation tasks, as well as strong transferability to sign language recognition.

1 Introduction

Sign language translation (SLT), recognition (SLR), and generation (SLG) are commonly treated as separate tasks, despite their shared sign–text mapping and inherent duality. SignFLIP proposes a unified LLM-centered framework and stage-wise training to refine shared representations, with competitive results across both translation and generation and transfer to recognition.

1 Introduction

  • Most prior work treats SLT, SLR, and SLG as individual problems, often using task-specific architectures to meet their distinct modeling requirements.This separation leads to repeated modeling effort and limits the ability to benefit from SLT–SLG duality.Source: §1 · p. 1
  • The paper asks whether a general-purpose LLM can serve as a shared foundation for sign language processing, despite sign languages’ underrepresentation in LLM pretraining corpora.Recent fine-tuning and prompt-engineering work suggests ways to adapt general-purpose models to the sign language domain.Source: §1 · p. 1
  • SignFLIP combines a unified architecture with large-scale, stage-wise training to progressively refine shared sign–text representations.Pre-alignment facilitates SLT, and the SLT-adapted representation further benefits SLG.Source: §1 · p. 2
  • SignFLIP achieves competitive performance on SLT and SLG and learns representations that benefit isolated and continuous SLR.Experiments span multiple main benchmarks, including CSL-Daily and How2Sign.Source: §1 · p. 2

2 Related Work

Prior sign language pretraining has mainly supported sign language understanding, while unified approaches to translation and generation remain relatively underexplored. Related work also describes gloss-based and gloss-free approaches to translation and generation, and identifies SignFLIP as an LLM-centered effort to unify the two directions.

2.1 Sign Language Pretraining

  • Earlier sign language pretraining used self-supervised or weakly supervised methods on relatively small corpora, while newer work increasingly pretrains on large, often noisy, out-of-domain datasets before in-domain fine-tuning.These advances predominantly benefit sign language understanding; pretraining directly supporting both SLT and SLG remains underexplored.Source: §2.1 · p. 2

2.2 Sign Language Translation

  • Gloss-based SLT uses gloss as intermediate supervision, but limited paired sign–gloss–text data constrains its scalability across downstream tasks.Gloss-free SLT directly maps signs to spoken text and has faced challenging sign–text alignment; SignFLIP focuses on gloss-free SLT and large-scale pretraining.Source: §2.2 · p. 2

2.3 Sign Language Generation

  • SLG research includes gloss-based pipelines and newer gloss-free approaches, which have begun to achieve results comparable to gloss-based methods, particularly with LLMs.Few studies jointly design translation and generation modules; SignFLIP advances unified bidirectional modeling with an LLM-centered architecture trained on large datasets.Source: §2.3 · p. 3

3 SignFLIP

SignFLIP unifies sign language translation and generation through a symmetric model and a three-stage pipeline that progressively aligns sign and text representations. Pretraining, translation adaptation, and pose generation build on this shared representation.

3.1 Model Architecture

  • SignFLIP uses a sign pose encoder, mT5 encoder and decoder, and a mirrored pose decoder to support translation and generation.For translation, pose-derived features enter mT5 for autoregressive text generation; for generation, text features guide pose-sequence length prediction and pose decoding.Source: §3.1 · pp. 3–4

3.2 Stage 1: Pretraining

  • Stage 1 jointly trains symmetric pose encoder and decoder components for semantic-aware pose reconstruction and sign–text pre-alignment.Consecutive frames are masked within a 24-frame window so reconstruction must use surrounding context; contrastive learning aligns sign representations with LLM text features.Source: §3.2 · p. 4

3.3 Training on Downstream Task

  • Stage 2 adapts the pose encoder and language model to SLT, first training on a large noisy corpus and then fine-tuning on the target dataset.The stage is intended to preserve translation capability while adapting mT5 to pose features for subsequent generation.Source: §3.3 · p. 4
  • Stage 3 generates sign poses non-autoregressively by predicting sequence length, cross-attending learnable pose tokens to text features, and decoding pose features.Feature distillation supervises generated pose features, while a hand-specific relative-coordinate loss increases the penalty for hand errors.Source: §3.3 · pp. 4–5

4 Experiments

Across SLT and SLG benchmarks, SignFLIP is competitive with prior methods, while its learned representations also transfer to recognition. The experiments assess datasets and metrics across tasks, compare generated poses, and test the effects of staged training.

4.1 Implementations and Preprocessing

  • For pretraining, the experiments use CSL-News and YouTubeASL, extract whole-body keypoints with RTMPose-x, and use mT5-Base on four NVIDIA H100 GPUs.The paper places stage-specific training recipes in Appendix A.Source: §4.1 · p. 5

4.2 Datasets and Evaluations

  • Evaluations cover CSL-Daily, OpenASL, How2Sign, and WLASL100, using translation, pose-generation, back-translation, and human-rating measures.Two professional ASL and CSL signers rated 15 generated samples on semantic accuracy and kinematic quality.Source: §4.2 · p. 5

4.3 Comparison with Methods

  • SignFLIP sets a new pose-only SLT state of the art on How2Sign, remains competitive on CSL-Daily, and outperforms selected prior 2D-pose methods on SLG at kinematic and semantic levels.The CSL-Daily results remain competitive with RGB-based and multimodal approaches; SLG metric scales differ across datasets, so cross-dataset comparisons are challenging.Source: §4.3 · p. 5, Table 2 · p. 6, Table 1 · p. 6
  • Human evaluations rate SignFLIP poses at 1.69 on How2Sign and 1.94 on CSL-Daily, while ground-truth poses score 3.48 and 3.44, respectively.The authors note that these ratings suggest a limitation of 2D pose keypoints for comprehensibility and highlight finger movements as important for precise meaning.Source: §4.3 · pp. 6–7, Table 4 · p. 7
  • With encoders frozen, SignFLIP reaches 28.8 test WER on CSL-Daily CSLR and 88.87 P-I and 89.16 P-C Top-1 accuracy on WLASL100 ISLR.The experiments attach task-specific decoders and report transfer to continuous and isolated recognition.Source: §4.3 · p. 7, Table 5 · p. 8, Table 6 · p. 8

4.4 Ablation Study

  • Pretraining yields +3.37 BLEU-4 on CSL-News and +4.26 BLEU-4 on YouTube ASL; pretraining and SLT training improve SLG DTW performance by 11% and 16%, respectively.The SLT tests use challenging, noisy datasets; the generation ablation is on CSL-Daily.Source: §4.4 · p. 8, Table 7 · p. 8, Table 8 · p. 8

5 Conclusion

SignFLIP is a large-scale unified framework for sign language translation and generation, trained on 600∼700K sign–text samples. The paper reports competitive results across benchmarks and promising transferability to recognition tasks.

5 Conclusion

  • SignFLIP uses a pretrained LLM in a unified translation-and-generation framework trained on 600∼700K sign–text samples.The authors describe the learned representations as generalizable and potentially useful as a foundation for sign language processing.Source: §5 · p. 9
  • Stage-wise training enhances translation and generation performance, with competitive results on several benchmarks.The conclusion describes the training strategy as improving performance across both tasks.Source: §5 · p. 9
  • SignFLIP shows promising transferability to both continuous and isolated sign language recognition.The authors present this transferability as evidence of generalizable sign representations.Source: §5 · p. 9

Limitations

The authors identify limitations in dataset representativeness, the understandability of 2D pose representations, and the quality of generated hand motion.

Limitations

  • Many sign language datasets are not primarily based on Deaf and hard-of-hearing signers, limiting their representativeness of real-world sign use.CSL-Daily is produced by interpreters, with text serving as the original source.Source: Limitations · p. 9
  • Even ground-truth samples received relatively low linguistic-accuracy scores in human evaluation, suggesting 2D poses may be difficult for sign language users to understand.The authors plan to explore 3D avatars for more effective and accessible pose generation.Source: Limitations · p. 9
  • Generated hand movements have limited amplitude and tend to collapse toward mean poses.The authors suggest this may be partly attributable to the LLM biasing outputs toward overly smooth, averaged motion patterns.Source: Limitations · p. 9

A Training Recipe of Each Stage

The appendix identifies a table of per-stage training recipes and reports the loss-weight hyperparameters.

A Training Recipe of Each Stage

B Statistics of Experimental Datasets

The appendix summarizes experimental dataset statistics and identifies the selected ASL and CSL datasets.

B Statistics of Experimental Datasets

  • Table 11 summarizes language, sentence-sample counts, vocabulary sizes, recording hours, and data sources for the experimental datasets.These are the reported categories of dataset statistics.Source: B Statistics of Experimental Datasets · p. 12, Table 11 · p. 12
  • The appendix includes a table of selected ASL and CSL datasets.The supplied passage does not provide the table's individual dataset entries.Source: Table 11 · p. 12

C SLT result on OpenASL

Table 12 presents SignFLIP’s SLT performance on OpenASL after Stage-2 fine-tuning.

C SLT result on OpenASL

  • Table 12 reports SignFLIP’s OpenASL SLT performance after Stage-2 fine-tuning.The supplied passage does not state the table’s results.Source: C SLT result on OpenASL · p. 12

D Ablation Study: MPM Window Size Impact

The ablation examines the MPM masking window and reports that a 24-frame window gives the best loss convergence and reconstruction results.

D Ablation Study: MPM Window Size Impact

  • During pretraining, SignFLIP randomly masks n consecutive frames within a 24-frame window to help pose features capture semantic context.The ablation evaluates the impact of window size on CSL-News.Source: D Ablation Study: MPM Window Size Impact · p. 12
  • A 24-frame MPM window achieves optimal loss convergence and reconstruction results by balancing sign reconstruction with broader semantic context.Other window sizes overemphasize one objective and yield suboptimal performance.Source: D Ablation Study: MPM Window Size Impact · p. 12

E Ablation Study: Loss for SLG

The section analyzes SLG training losses, presents translation and generation case studies, describes the human evaluation questionnaire, and notes risks from data-diversity and modeling-accuracy limitations.

E Ablation Study: Loss for SLG

  • Feature distillation yields a more pronounced generation improvement than hand loss, with multi-perspective feature supervision providing clearer optimization targets than coarse output-pose-only optimization.The authors attribute this difference to the difficulty of converging when optimization operates solely at the output-pose level.Source: E Ablation Study: Loss for SLG · pp. 12–13

F Case Study of Translation

H Human Evaluation Questionnaire

  • The questionnaire rates generated sign pose sequences on a 1–5 scale across semantic accuracy, motion smoothness, anatomical plausibility, and hand clarity.Scores range from 1 for very poor quality to 5 for excellent quality.Source: H Human Evaluation Questionnaire · p. 13

G Case Study of Generation

I Potential Risks

  • The authors caution that limited data diversity and modeling accuracy leave current sign language processing vulnerable to translation or generation inaccuracies and misinterpretations with potential negative social impacts.Source: I Potential Risks · p. 14

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