Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2303.11579
In this paper, a novel Diffusion-based 3D Pose estimation (D3DP) method with Joint-wise reProjection-based Multi-hypothesis Aggregation (JPMA) is proposed for probabilistic 3D human pose estimation. On the one hand, D3DP generates multiple possible 3D pose hypotheses for a single 2D observation. It gradually diffuses the ground truth 3D poses to a random distribution, and learns a denoiser conditioned on 2D keypoints to recover the uncontaminated 3D poses. The proposed D3DP is compatible with existing 3D pose estimators and supports users to balance efficiency and accuracy during inference through two customizable parameters. On the other hand, JPMA is proposed to assemble multiple hypotheses generated by D3DP into a single 3D pose for practical use. It reprojects 3D pose hypotheses to the 2D camera plane, selects the best hypothesis joint-by-joint based on the reprojection errors, and combines the selected joints into the final pose. The proposed JPMA conducts aggregation at the joint level and makes use of the 2D prior information, both of which have been overlooked by previous approaches. Extensive experiments on Human3.6M and MPI-INF-3DHP datasets show that our method outperforms the state-of-the-art deterministic and probabilistic approaches by 1.5% and 8.9%, respectively. Code is available at https://github.com/paTRICK-swk/D3DP.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.11579
- https://arxiv.org/pdf/2303.11579
- OA Status
- green
- Cited By
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4353114104
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4353114104Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2303.11579Digital Object Identifier
- Title
-
Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis AggregationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-21Full publication date if available
- Authors
-
Wenkang Shan, Zhenhua Liu, Xinfeng Zhang, Zhao Wang, Kai Han, Shanshe Wang, Siwei Ma, Wen GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2303.11579Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2303.11579Direct 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/2303.11579Direct OA link when available
- Concepts
-
Pose, Computer science, Estimator, Probabilistic logic, Inference, Joint (building), Ground truth, Artificial intelligence, Code (set theory), Estimation, Joint probability distribution, Pattern recognition (psychology), Statistics, Mathematics, Economics, Engineering, Programming language, Architectural engineering, Set (abstract data type), ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 6, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.with | 11, 72 |
| abstract_inverted_index.8.9%, | 189 |
| abstract_inverted_index.based | 128 |
| abstract_inverted_index.final | 140 |
| abstract_inverted_index.hand, | 28, 94 |
| abstract_inverted_index.human | 22 |
| abstract_inverted_index.joint | 149 |
| abstract_inverted_index.level | 150 |
| abstract_inverted_index.makes | 152 |
| abstract_inverted_index.novel | 4 |
| abstract_inverted_index.other | 93 |
| abstract_inverted_index.pose. | 141 |
| abstract_inverted_index.poses | 48 |
| abstract_inverted_index.prior | 157 |
| abstract_inverted_index.truth | 46 |
| abstract_inverted_index.users | 79 |
| abstract_inverted_index.which | 161 |
| abstract_inverted_index.(D3DP) | 9 |
| abstract_inverted_index.(JPMA) | 16 |
| abstract_inverted_index.camera | 121 |
| abstract_inverted_index.during | 85 |
| abstract_inverted_index.ground | 45 |
| abstract_inverted_index.joints | 137 |
| abstract_inverted_index.learns | 54 |
| abstract_inverted_index.method | 10, 178 |
| abstract_inverted_index.paper, | 2 |
| abstract_inverted_index.plane, | 122 |
| abstract_inverted_index.poses. | 66 |
| abstract_inverted_index.random | 51 |
| abstract_inverted_index.single | 38, 107 |
| abstract_inverted_index.balance | 81 |
| abstract_inverted_index.errors, | 132 |
| abstract_inverted_index.recover | 62 |
| abstract_inverted_index.selects | 123 |
| abstract_inverted_index.through | 87 |
| abstract_inverted_index.accuracy | 84 |
| abstract_inverted_index.assemble | 99 |
| abstract_inverted_index.combines | 134 |
| abstract_inverted_index.conducts | 145 |
| abstract_inverted_index.datasets | 174 |
| abstract_inverted_index.denoiser | 56 |
| abstract_inverted_index.diffuses | 43 |
| abstract_inverted_index.existing | 73 |
| abstract_inverted_index.multiple | 31, 100 |
| abstract_inverted_index.possible | 32 |
| abstract_inverted_index.previous | 166 |
| abstract_inverted_index.proposed | 18, 68, 97, 143 |
| abstract_inverted_index.selected | 136 |
| abstract_inverted_index.supports | 78 |
| abstract_inverted_index.Extensive | 168 |
| abstract_inverted_index.Human3.6M | 171 |
| abstract_inverted_index.available | 193 |
| abstract_inverted_index.generated | 102 |
| abstract_inverted_index.generates | 30 |
| abstract_inverted_index.gradually | 42 |
| abstract_inverted_index.inference | 86 |
| abstract_inverted_index.keypoints | 60 |
| abstract_inverted_index.practical | 111 |
| abstract_inverted_index.Joint-wise | 12 |
| abstract_inverted_index.approaches | 185 |
| abstract_inverted_index.compatible | 71 |
| abstract_inverted_index.efficiency | 82 |
| abstract_inverted_index.estimation | 8 |
| abstract_inverted_index.estimators | 76 |
| abstract_inverted_index.hypotheses | 35, 101, 117 |
| abstract_inverted_index.hypothesis | 126 |
| abstract_inverted_index.overlooked | 164 |
| abstract_inverted_index.reprojects | 114 |
| abstract_inverted_index.Aggregation | 15 |
| abstract_inverted_index.aggregation | 146 |
| abstract_inverted_index.approaches. | 167 |
| abstract_inverted_index.conditioned | 57 |
| abstract_inverted_index.estimation. | 24 |
| abstract_inverted_index.experiments | 169 |
| abstract_inverted_index.outperforms | 179 |
| abstract_inverted_index.parameters. | 90 |
| abstract_inverted_index.MPI-INF-3DHP | 173 |
| abstract_inverted_index.customizable | 89 |
| abstract_inverted_index.information, | 158 |
| abstract_inverted_index.observation. | 40 |
| abstract_inverted_index.reprojection | 131 |
| abstract_inverted_index.deterministic | 182 |
| abstract_inverted_index.distribution, | 52 |
| abstract_inverted_index.probabilistic | 20, 184 |
| abstract_inverted_index.respectively. | 190 |
| abstract_inverted_index.joint-by-joint | 127 |
| abstract_inverted_index.uncontaminated | 64 |
| abstract_inverted_index.Diffusion-based | 5 |
| abstract_inverted_index.Multi-hypothesis | 14 |
| abstract_inverted_index.state-of-the-art | 181 |
| abstract_inverted_index.reProjection-based | 13 |
| abstract_inverted_index.https://github.com/paTRICK-swk/D3DP. | 195 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |