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STK LSF: A Motion Capture Dataset in LSF for SignToKids

Reverdy, Clément, Gibet, Sylvie, Le Naour, Thibaut·2024·Proceedings of the Language Resources and Evaluation Conference
DOI 10.63317/4y9dcusqas55
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Abstract

This article presents a new bilingual dataset in written French and French Sign Language (LSF), called STK LSF . This corpus is currently being produced as part of the SignToKids project. The aim of this corpus is to provide digital educational tools for deaf children, thereby facilitating the joint learning of LSF and written French. More broadly, it is intended to support future studies on the automatic processing of signed languages. To define this corpus, we focused on several grammatical phenomena typical to LSF, as well as in tales usually studied by hearing children in the second cycle in France. The corpus data represent approximately 1 hour of recording, carried out with a motion capture system (MoCap) offering a spatial precision of less than 1 mm and a temporal precision of 240 Hz. This high level of precision guarantees the quality of the data collected, which will be used both to build pedagogical scenarios in French and LSF, including signing avatar videos, and for automatic translation of text into LSF.

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Research works printed but not linked: 21

  1. É. Bertin-Lemée, A. Braffort, C. Challant, C. Danet, B. Dauriac, M. Filhol, E. Martinod, and J. Segouat. 2022. Rosetta-lsf: an aligned corpus of french sign language and french for text-to- sign translation. In 13th Conference on Lan- guage Resources and Evaluation (LREC 2022).
  2. H. Brock and K. Nakadai. 2018. Deep jslc: A multi- modal corpus collection for data-driven genera- tion of japanese sign language expressions. In Proceedings of the Eleventh International Con- ference on Language Resources and Evaluation (LREC 2018).
  3. H. Bull, A. Braffort, and M. Gouiffès. 2020. Mediapi- skel-a 2d-skeleton video database of french sign language with aligned french subtitles. In Pro- ceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 6063-6068.
  4. Z. Cao, T. Simon, S.E. Wei, and Y. Sheikh. 2017. Realtime multi-person 2d pose estimation using part affinity fields. pages 7291-7299.
  5. K. Duarte and S. Gibet. 2010. Heterogeneous data sources for signed language analysis and synthe- sis: The signcom project. In 7th Int. Conf. on Lan- guage Resources and Evaluation (LREC 2010), volume 2, pages 1-8. European Language Re- sources Association.
  6. J. Fink, B. Frénay, L. Meurant, and A. Cleve. 2021. Lsfb-cont and lsfb-isol: Two new datasets for vision-based sign language recognition. In 2021 International Joint Conference on Neural Net- works (IJCNN), pages 1-8. IEEE.
  7. S. Gibet, N. Courty, K. Duarte, and T. Le Naour. 2011. The signcom system for data-driven an- imation of interactive virtual signers : Method- ology and evaluation. In ACM Transactions on Interactive Intelligent Systems, volume 1.
  8. T. Hanke, M. Schulder, R. Konrad, and E. Jahn. 2020. Extending the public dgs corpus in size and depth. In sign-lang@ LREC 2020, pages 75- 82. European Language Resources Association (ELRA).
  9. P. Jedlička, Z. Krňoul, J. Kanis, and M. Železnỳ. 2020. Sign language motion capture dataset for data-driven synthesis. In Workshop on the Rep- resentation and Processing of Sign Languages (LREC2020), pages 101-106.
  10. Y. Kartynnik, A. Ablavatski, I. Grishchenko, and M. Grundmann. 2019. Real-time facial surface geometry from monocular video on mobile gpus. arXiv preprint arXiv:1907.06724. · arXiv
  11. M. Kopf, M. Schulder, and T. Hanke. 2022. The sign language dataset compendium: Creating an overview of digital linguistic resources. In Work- shop on the Representation and Processing of Sign Languages (LREC2022), pages 102-109.
  12. F. Lefebvre-Albaret, S. Gibet, A. Turki, L. Ha- mon, and R. Brun. 2013. Overview of the sign3d project high-fidelity 3d recording, index- ing and editing of french sign language con- tent. In Third International Symposium on Sign Language Translation and Avatar Technology (SLTAT) 2013.
  13. P. Lu and M. Huenerfauth. 2010. Collecting a motion-capture corpus of american sign lan- guage for data-driven generation research. In Proceedings of the NAACL HLT 2010 workshop on speech and language processing for assistive technologies, pages 89-97.
  14. P. Lu and M. Huenerfauth. 2014. Collecting and evaluating the cuny asl corpus for research on american sign language animation. Computer Speech & Language, 28(3):812-831.
  15. L. Naert, C. Larboulette, and S. Gibet. 2020. Lsf- animal: A motion capture corpus in french sign language designed for the animation of signing avatars. In Proceedings of The 12th Language Resources and Evaluation Conference, pages 6008-6017.
  16. L. Naert, C. Larboulette, and S. Gibet. 2021. Mo- tion synthesis and editing for the generation of new sign language content. Machine Translation, 35(3):405-430. · DOI
  17. L. Naert, C. Reverdy, C. Larboulette, and S. Gibet. 2018. Per channel automatic annotation of sign language motion capture data. In Workshop on the Representation and Processing of Sign Languages: Involving the Language Community (LREC 2018), Miyazaki, Japan.
  18. F. Nunnari, M. Filhol, and A. Heloir. 2018. Ani- mating azee descriptions using off-the-shelf ik solvers. In Workshop on the Representation and Processing of Sign Languages: Involving the Language Community (SignLang 2018), (LREC 2018), pages 7-12.
  19. G. Pavlakos, V. Choutas, N. Ghorbani, T. Bolkart, A.A.A. Osman, D. Tzionas, and M.J. Black. 2019. Expressive body capture: 3d hands, face, and body from a single image. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10975-10985.
  20. C. Reverdy. 2019. Annotation et synthèse basée données des expressions faciales de la Langue des Signes Française. Ph.D. thesis, Université de Bretagne Sud.
  21. T. Weise, S. Bouaziz, H. Li, and M. Pauly. 2011. Realtime performance-based facial animation. ACM transactions on graphics (TOG), 30(4):1- 10.

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Metadata

Authors
Reverdy, C., Gibet, S., & Le Naour, T.
Venue
Proceedings of the Language Resources and Evaluation Conference
Publisher
European Language Resources Association (ELRA) and ICCL
Published
2024-05
Pages
315-322
ISSN
2522-2686
DOI
10.63317/4y9dcusqas55
Cited by
0
References
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CoChat. (2026). Citation check: STK LSF: A Motion Capture Dataset in LSF for SignToKids. https://app.cochat.ai/papers/10.63317/4y9dcusqas55
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Reverdy, C., Gibet, S., & Le Naour, T. (2024). STK LSF: A Motion Capture Dataset in LSF for SignToKids. Proceedings of the Language Resources and Evaluation Conference. https://doi.org/10.63317/4y9dcusqas55

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