DC Field | Value | Language |
---|---|---|
dc.contributor.author | Woo, Taeyun | ko |
dc.contributor.author | Park, Wonjung | ko |
dc.contributor.author | Jeong, Woohyun | ko |
dc.contributor.author | Park, Jinah | ko |
dc.date.accessioned | 2023-11-01T09:00:13Z | - |
dc.date.available | 2023-11-01T09:00:13Z | - |
dc.date.created | 2023-11-01 | - |
dc.date.created | 2023-11-01 | - |
dc.date.issued | 2023-11 | - |
dc.identifier.citation | COMPUTERS & GRAPHICS-UK, v.116, pp.474 - 490 | - |
dc.identifier.issn | 0097-8493 | - |
dc.identifier.uri | http://hdl.handle.net/10203/314023 | - |
dc.description.abstract | The research topic of estimating hand pose from the images of hand-object interaction has the potential for replicating natural hand behavior in many practical applications of virtual reality and robotics. However, the intricacy of hand-object interaction combined with mutual occlusion, and the need for physical plausibility, brings many challenges to the problem. This paper provides a comprehensive survey of the state-of-the-art deep learning-based approaches for estimating hand pose (joint and shape) in the context of hand-object interaction. We discuss various deep learning-based approaches to image-based hand tracking, including hand joint and shape estimation. In addition, we review the hand-object interaction dataset benchmarks that are well-utilized in hand joint and shape estimation methods. Deep learning has emerged as a powerful technique for solving many problems including hand pose estimation. While we cover extensive research in the field, we discuss the remaining challenges leading to future research directions. | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | A survey of deep learning methods and datasets for hand pose estimation from hand-object interaction images | - |
dc.type | Article | - |
dc.identifier.wosid | 001092850900001 | - |
dc.identifier.scopusid | 2-s2.0-85174541065 | - |
dc.type.rims | ART | - |
dc.citation.volume | 116 | - |
dc.citation.beginningpage | 474 | - |
dc.citation.endingpage | 490 | - |
dc.citation.publicationname | COMPUTERS & GRAPHICS-UK | - |
dc.identifier.doi | 10.1016/j.cag.2023.09.013 | - |
dc.contributor.localauthor | Park, Jinah | - |
dc.contributor.nonIdAuthor | Jeong, Woohyun | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Hand -object pose | - |
dc.subject.keywordAuthor | Reconstruction | - |
dc.subject.keywordAuthor | Computer vision | - |
dc.subject.keywordAuthor | Benchmark dataset | - |
dc.subject.keywordPlus | RECONSTRUCTION | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | TRACKING | - |
dc.subject.keywordPlus | MOTION | - |
dc.subject.keywordPlus | GRASP | - |
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