Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey Article Swipe
Takehiko Ohkawa
,
Ryosuke Furuta
,
Yoichi Sato
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s11263-023-01856-0
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s11263-023-01856-0
In this survey, we present a systematic review of 3D hand pose estimation from the perspective of efficient annotation and learning. 3D hand pose estimation has been an important research area owing to its potential to enable various applications, such as video understanding, AR/VR, and robotics. However, the performance of models is tied to the quality and quantity of annotated 3D hand poses. Under the status quo, acquiring such annotated 3D hand poses is challenging, e.g., due to the difficulty of 3D annotation and the presence of occlusion. To reveal this problem, we review the pros and cons of existing annotation methods classified as manual, synthetic-model-based, hand-sensor-based, and computational approaches. Additionally, we examine methods for learning 3D hand poses when annotated data are scarce, including self-supervised pretraining, semi-supervised learning, and domain adaptation. Based on the study of efficient annotation and learning, we further discuss limitations and possible future directions in this field.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11263-023-01856-0
- https://link.springer.com/content/pdf/10.1007/s11263-023-01856-0.pdf
- OA Status
- hybrid
- Cited By
- 14
- References
- 109
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385632207
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385632207Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11263-023-01856-0Digital Object Identifier
- Title
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Efficient Annotation and Learning for 3D Hand Pose Estimation: A SurveyWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-07Full publication date if available
- Authors
-
Takehiko Ohkawa, Ryosuke Furuta, Yoichi SatoList of authors in order
- Landing page
-
https://doi.org/10.1007/s11263-023-01856-0Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s11263-023-01856-0.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s11263-023-01856-0.pdfDirect OA link when available
- Concepts
-
Annotation, Computer science, Artificial intelligence, Machine learning, Pose, Domain adaptation, Domain (mathematical analysis), Deep learning, Robotics, Classifier (UML), Robot, Mathematical analysis, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 7, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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109Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5040126789 |
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