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Text-to-Korean Sign Language Pose Sequence Generation Using Non-Manual Signal Conditioning and Multi-Scale Temporal

Seungju Lee1, Gooman Park1

  • 1Department of Smart ICT Convergence Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea.

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Summary

This study introduces a new model for generating Korean Sign Language (KSL) poses from text, improving accuracy and naturalness by considering non-manual signals and temporal coherence. The advanced text-to-pose model enhances information accessibility for the deaf and hard-of-hearing community.

Keywords:
Korean sign languageaccessibilitylength predictionmulti-scale temporal refinementnon-manual signalssign language generationtext-to-pose generation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Linguistics

Background:

  • Automatic sign language generation aids information accessibility for deaf and hard-of-hearing individuals.
  • Generating sign language poses from text is complex due to manual and non-manual signals, and temporal coherence requirements.
  • Existing methods struggle with the non-linear relationship between text length and pose sequence duration.

Purpose of the Study:

  • To propose a novel text-to-Korean Sign Language (KSL) pose generation model.
  • To address challenges in sign language generation, including non-manual signals and temporal dynamics.
  • To improve the accuracy and naturalness of avatar-based sign language expression.

Main Methods:

  • Developed a framework integrating text encoding, pose decoding, non-manual signal conditioning, and multi-scale temporal refinement.
  • The model generates normalized 58-joint KSL keypoint sequences from morpheme-level text.
  • Joint optimization included pose reconstruction, motion continuity, bone consistency, precision, non-manual signal prediction, and length consistency.

Main Results:

  • The proposed model significantly outperformed text-only and Transformer-based baselines in KSL pose generation.
  • Key metrics improved, including reduced Mean Per Joint Position Error (MPJPE) and Pose Mean Absolute Error (MAE).
  • Non-manual signal prediction (F1 score) saw a substantial increase, indicating better integration of facial expressions and body posture.

Conclusions:

  • Text-based KSL pose generation necessitates a holistic approach, integrating non-manual expressions, length consistency, and long-term temporal structure.
  • The model demonstrates a significant advancement over frame-wise keypoint prediction methods.
  • Further research is needed to validate linguistic meaningfulness and real-world accessibility beyond coordinate-level improvements.