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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.02421 |
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| _version_ | 1866917944841207808 |
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| author | Pratikaki, Chrysa Filntisis, Panagiotis Katsamanis, Athanasios Roussos, Anastasios Maragos, Petros |
| author_facet | Pratikaki, Chrysa Filntisis, Panagiotis Katsamanis, Athanasios Roussos, Anastasios Maragos, Petros |
| contents | Sign Languages are the primary form of communication for Deaf communities across the world. To break the communication barriers between the Deaf and Hard-of-Hearing and the hearing communities, it is imperative to build systems capable of translating the spoken language into sign language and vice versa. Building on insights from previous research, we propose a deep learning model for Sign Language Production (SLP), which to our knowledge is the first attempt on Greek SLP. We tackle this task by utilizing a transformer-based architecture that enables the translation from text input to human pose keypoints, and the opposite. We evaluate the effectiveness of the proposed pipeline on the Greek SL dataset Elementary23, through a series of comparative analyses and ablation studies. Our pipeline's components, which include data-driven gloss generation, training through video to text translation and a scheduling algorithm for teacher forcing - auto-regressive decoding seem to actively enhance the quality of produced SL videos. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Transformer-Based Framework for Greek Sign Language Production using Extended Skeletal Motion Representations Pratikaki, Chrysa Filntisis, Panagiotis Katsamanis, Athanasios Roussos, Anastasios Maragos, Petros Machine Learning Sign Languages are the primary form of communication for Deaf communities across the world. To break the communication barriers between the Deaf and Hard-of-Hearing and the hearing communities, it is imperative to build systems capable of translating the spoken language into sign language and vice versa. Building on insights from previous research, we propose a deep learning model for Sign Language Production (SLP), which to our knowledge is the first attempt on Greek SLP. We tackle this task by utilizing a transformer-based architecture that enables the translation from text input to human pose keypoints, and the opposite. We evaluate the effectiveness of the proposed pipeline on the Greek SL dataset Elementary23, through a series of comparative analyses and ablation studies. Our pipeline's components, which include data-driven gloss generation, training through video to text translation and a scheduling algorithm for teacher forcing - auto-regressive decoding seem to actively enhance the quality of produced SL videos. |
| title | A Transformer-Based Framework for Greek Sign Language Production using Extended Skeletal Motion Representations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.02421 |