Sign Language Motion Capture Dataset for Data-driven Synthesis

Pavel Jedlička, Zdeněk Krňoul, Jakub Kanis, Miloš Železný


Abstract
This paper presents a new 3D motion capture dataset of Czech Sign Language (CSE). Its main purpose is to provide the data for further analysis and data-based automatic synthesis of CSE utterances. The content of the data in the given limited domain of weather forecasts was carefully selected by the CSE linguists to provide the necessary utterances needed to produce any new weather forecast. The dataset was recorded using the state-of-the-art motion capture (MoCap) technology to provide the most precise trajectories of the motion. In general, MoCap is a device capable of accurate recording of motion directly in 3D space. The data contains trajectories of body, arms, hands and face markers recorded at once to provide consistent data without the need for the time alignment.
Anthology ID:
2020.signlang-1.16
Volume:
Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Eleni Efthimiou, Stavroula-Evita Fotinea, Thomas Hanke, Julie A. Hochgesang, Jette Kristoffersen, Johanna Mesch
Venue:
SignLang
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
101–106
Language:
English
URL:
https://aclanthology.org/2020.signlang-1.16
DOI:
Bibkey:
Cite (ACL):
Pavel Jedlička, Zdeněk Krňoul, Jakub Kanis, and Miloš Železný. 2020. Sign Language Motion Capture Dataset for Data-driven Synthesis. In Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives, pages 101–106, Marseille, France. European Language Resources Association (ELRA).
Cite (Informal):
Sign Language Motion Capture Dataset for Data-driven Synthesis (Jedlička et al., SignLang 2020)
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PDF:
https://aclanthology.org/2020.signlang-1.16.pdf