Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation Article Swipe
Juze Zhang
,
Jingya Wang
,
Shi Ye
,
Fei Gao
,
Lan Xu
,
Jingyi Yu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.07900
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.07900
Inter-person occlusion and depth ambiguity make estimating the 3D poses of monocular multiple persons as camera-centric coordinates a challenging problem. Typical top-down frameworks suffer from high computational redundancy with an additional detection stage. By contrast, the bottom-up methods enjoy low computational costs as they are less affected by the number of humans. However, most existing bottom-up methods treat camera-centric 3D human pose estimation as two unrelated subtasks: 2.5D pose estimation and camera-centric depth estimation. In this paper, we propose a unified model that leverages the mutual benefits of both these subtasks. Within the framework, a robust structured 2.5D pose estimation is designed to recognize inter-person occlusion based on depth relationships. Additionally, we develop an end-to-end geometry-aware depth reasoning method that exploits the mutual benefits of both 2.5D pose and camera-centric root depths. This method first uses 2.5D pose and geometry information to infer camera-centric root depths in a forward pass, and then exploits the root depths to further improve representation learning of 2.5D pose estimation in a backward pass. Further, we designed an adaptive fusion scheme that leverages both visual perception and body geometry to alleviate inherent depth ambiguity issues. Extensive experiments demonstrate the superiority of our proposed model over a wide range of bottom-up methods. Our accuracy is even competitive with top-down counterparts. Notably, our model runs much faster than existing bottom-up and top-down methods.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2207.07900
- https://arxiv.org/pdf/2207.07900
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286233867
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4286233867Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2207.07900Digital Object Identifier
- Title
-
Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose EstimationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-16Full publication date if available
- Authors
-
Juze Zhang, Jingya Wang, Shi Ye, Fei Gao, Lan Xu, Jingyi YuList of authors in order
- Landing page
-
https://arxiv.org/abs/2207.07900Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2207.07900Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2207.07900Direct OA link when available
- Concepts
-
Pose, Artificial intelligence, Computer vision, Computer science, Monocular, Ambiguity, Exploit, Redundancy (engineering), 3D pose estimation, Mutual information, Operating system, Computer security, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.bottom-up | 36, 55, 204, 222 |
| abstract_inverted_index.contrast, | 34 |
| abstract_inverted_index.detection | 31 |
| abstract_inverted_index.leverages | 83, 177 |
| abstract_inverted_index.monocular | 11 |
| abstract_inverted_index.occlusion | 1, 105 |
| abstract_inverted_index.reasoning | 117 |
| abstract_inverted_index.recognize | 103 |
| abstract_inverted_index.subtasks. | 90 |
| abstract_inverted_index.subtasks: | 66 |
| abstract_inverted_index.unrelated | 65 |
| abstract_inverted_index.additional | 30 |
| abstract_inverted_index.end-to-end | 114 |
| abstract_inverted_index.estimating | 6 |
| abstract_inverted_index.estimation | 62, 69, 99, 164 |
| abstract_inverted_index.framework, | 93 |
| abstract_inverted_index.frameworks | 22 |
| abstract_inverted_index.perception | 180 |
| abstract_inverted_index.redundancy | 27 |
| abstract_inverted_index.structured | 96 |
| abstract_inverted_index.challenging | 18 |
| abstract_inverted_index.competitive | 210 |
| abstract_inverted_index.coordinates | 16 |
| abstract_inverted_index.demonstrate | 192 |
| abstract_inverted_index.estimation. | 73 |
| abstract_inverted_index.experiments | 191 |
| abstract_inverted_index.information | 140 |
| abstract_inverted_index.superiority | 194 |
| abstract_inverted_index.Inter-person | 0 |
| abstract_inverted_index.inter-person | 104 |
| abstract_inverted_index.Additionally, | 110 |
| abstract_inverted_index.computational | 26, 40 |
| abstract_inverted_index.counterparts. | 213 |
| abstract_inverted_index.camera-centric | 15, 58, 71, 129, 143 |
| abstract_inverted_index.geometry-aware | 115 |
| abstract_inverted_index.relationships. | 109 |
| abstract_inverted_index.representation | 159 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |