HIDE: Hierarchical iterative decoding enhancement for multi‐view 3D human parameter regression.
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| Title: | HIDE: Hierarchical iterative decoding enhancement for multi‐view 3D human parameter regression. |
|---|---|
| Authors: | Lin, Weitao1,2 (AUTHOR), Zhang, Jiguang1,2 (AUTHOR) jiguang.zhang@ia.ac.cn, Meng, Weiliang1,2 (AUTHOR) weiliang.meng@ia.ac.cn, Liu, Xianglong2 (AUTHOR), Zhang, Xiaopeng1,2 (AUTHOR) |
| Source: | Computer Animation & Virtual Worlds. May2024, Vol. 35 Issue 3, p1-13. 13p. |
| Subjects: | Iterative decoding, Joints (Anatomy), Computer vision, Human body, Computer simulation |
| Abstract: | Parametric human modeling are limited to either single‐view frameworks or simple multi‐view frameworks, failing to fully leverage the advantages of easily trainable single‐view networks and the occlusion‐resistant capabilities of multi‐view images. The prevalent presence of object occlusion and self‐occlusion in real‐world scenarios leads to issues of robustness and accuracy in predicting human body parameters. Additionally, many methods overlook the spatial connectivity of human joints in the global estimation of model pose parameters, resulting in cumulative errors in continuous joint parameters.To address these challenges, we propose a flexible and efficient iterative decoding strategy. By extending from single‐view images to multi‐view video inputs, we achieve local‐to‐global optimization. We utilize attention mechanisms to capture the rotational dependencies between any node in the human body and all its ancestor nodes, thereby enhancing pose decoding capability. We employ a parameter‐level iterative fusion of multi‐view image data to achieve flexible integration of global pose information, rapidly obtaining appropriate projection features from different viewpoints, ultimately resulting in precise parameter estimation. Through experiments, we validate the effectiveness of the HIDE method on the Human3.6M and 3DPW datasets, demonstrating significantly improved visualization results compared to previous methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Animation & Virtual Worlds is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 178072306 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: HIDE: Hierarchical iterative decoding enhancement for multi‐view 3D human parameter regression. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lin%2C+Weitao%22">Lin, Weitao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jiguang%22">Zhang, Jiguang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jiguang.zhang@ia.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Meng%2C+Weiliang%22">Meng, Weiliang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> weiliang.meng@ia.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Xianglong%22">Liu, Xianglong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaopeng%22">Zhang, Xiaopeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Animation+%26+Virtual+Worlds%22">Computer Animation & Virtual Worlds</searchLink>. May2024, Vol. 35 Issue 3, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Iterative+decoding%22">Iterative decoding</searchLink><br /><searchLink fieldCode="DE" term="%22Joints+%28Anatomy%29%22">Joints (Anatomy)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Human+body%22">Human body</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Parametric human modeling are limited to either single‐view frameworks or simple multi‐view frameworks, failing to fully leverage the advantages of easily trainable single‐view networks and the occlusion‐resistant capabilities of multi‐view images. The prevalent presence of object occlusion and self‐occlusion in real‐world scenarios leads to issues of robustness and accuracy in predicting human body parameters. Additionally, many methods overlook the spatial connectivity of human joints in the global estimation of model pose parameters, resulting in cumulative errors in continuous joint parameters.To address these challenges, we propose a flexible and efficient iterative decoding strategy. By extending from single‐view images to multi‐view video inputs, we achieve local‐to‐global optimization. We utilize attention mechanisms to capture the rotational dependencies between any node in the human body and all its ancestor nodes, thereby enhancing pose decoding capability. We employ a parameter‐level iterative fusion of multi‐view image data to achieve flexible integration of global pose information, rapidly obtaining appropriate projection features from different viewpoints, ultimately resulting in precise parameter estimation. Through experiments, we validate the effectiveness of the HIDE method on the Human3.6M and 3DPW datasets, demonstrating significantly improved visualization results compared to previous methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Animation & Virtual Worlds is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=178072306 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/cav.2266 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Iterative decoding Type: general – SubjectFull: Joints (Anatomy) Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Human body Type: general – SubjectFull: Computer simulation Type: general Titles: – TitleFull: HIDE: Hierarchical iterative decoding enhancement for multi‐view 3D human parameter regression. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lin, Weitao – PersonEntity: Name: NameFull: Zhang, Jiguang – PersonEntity: Name: NameFull: Meng, Weiliang – PersonEntity: Name: NameFull: Liu, Xianglong – PersonEntity: Name: NameFull: Zhang, Xiaopeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 15464261 Numbering: – Type: volume Value: 35 – Type: issue Value: 3 Titles: – TitleFull: Computer Animation & Virtual Worlds Type: main |
| ResultId | 1 |
