How2Sign: a large-scale multimodal dataset for continuous American Sign Language
How2Sign consists of a parallel corpus of 80 hours of sign language videos (collected with multi-view RGB and depth sensor data) with corresponding speech transcriptions and gloss annotations. In addition, a three-hour subset was further recorded in a geodesic dome setup using hundreds of cameras an...
| Autores: | , , , , , , |
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| Formato: | conjunto de datos |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Consorci de Serveis Universitaris de Catalunya (CSUC) |
| Repositorio: | CORA.Repositori de Dades de Recerca |
| OAI Identifier: | oai:dnet:cora.rdr____::bbca15bbcd803bb46705b03c179c61ed |
| Acesso em linha: | https://doi.org/10.34810/DATA33 |
| Access Level: | acceso abierto |
| Palavra-chave: | Computer and Information Science American Sign Language Machine translation Computer vision Natural language processing Sign language Gesture recognition |
| Resumo: | How2Sign consists of a parallel corpus of 80 hours of sign language videos (collected with multi-view RGB and depth sensor data) with corresponding speech transcriptions and gloss annotations. In addition, a three-hour subset was further recorded in a geodesic dome setup using hundreds of cameras and sensors, which enables detailed 3D reconstruction and pose estimation and paves the way for vision systems to understand the 3D geometry of sign language. |
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