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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2022.3149229
|2 doi
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|a pubmed24n1122.xml
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|a (DE-627)NLM336756097
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Yang, Kaibing
|e verfasserin
|4 aut
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| 245 |
1 |
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|a LASOR
|b Learning Accurate 3D Human Pose and Shape via Synthetic Occlusion-Aware Data and Neural Mesh Rendering
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 18.02.2022
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|a Date Revised 18.02.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a A key challenge in the task of human pose and shape estimation is occlusion, including self-occlusions, object-human occlusions, and inter-person occlusions. The lack of diverse and accurate pose and shape training data becomes a major bottleneck, especially for scenes with occlusions in the wild. In this paper, we focus on the estimation of human pose and shape in the case of inter-person occlusions, while also handling object-human occlusions and self-occlusion. We propose a novel framework that synthesizes occlusion-aware silhouette and 2D keypoints data and directly regress to the SMPL pose and shape parameters. A neural 3D mesh renderer is exploited to enable silhouette supervision on the fly, which contributes to great improvements in shape estimation. In addition, keypoints-and-silhouette-driven training data in panoramic viewpoints are synthesized to compensate for the lack of viewpoint diversity in any existing dataset. Experimental results show that we are among the state-of-the-art on the 3DPW and 3DPW-Crowd datasets in terms of pose estimation accuracy. The proposed method evidently outperforms Mesh Transformer, 3DCrowdNet and ROMP in terms of shape estimation. Top performance is also achieved on SSP-3D in terms of shape prediction accuracy. Demo and code will be available at https://igame-lab.github.io/LASOR/
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Gu, Renshu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Maoyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Toyoura, Masahiro
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Gang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 10., Seite 1938-1948
|w (DE-627)NLM09821456X
|x 1941-0042
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|g volume:31
|g year:2022
|g day:10
|g pages:1938-1948
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|u http://dx.doi.org/10.1109/TIP.2022.3149229
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