Shape-Aware Human Pose and Shape Reconstruction Using Multi-View Images
Junbang Liang, Ming C. Lin
2019年份
91被引次数
17顶会引用
摘要
We propose a scalable neural network framework to reconstruct the 3D mesh of a human body from multi-view images, in the subspace of the SMPL model [23] . Use of multi-view images can significantly reduce the projection ambiguity of the problem, increasing the reconstruction accuracy of the 3D human body under clothing. Our experiments show that this method benefits from the synthetic dataset generated from our pipeline since it has good flexibility of variable control and can provide ground-truth for validation. Our method outperforms existing methods on real-world images, especially on shape estimations.
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引用它的顶会 Paper17
- Conditional Directed Graph Convolution for 3D Human Pose EstimationWenbo Hu, Changgong Zhang, Fangneng Zhan, Lei Zhang 等ACM MM 2021 · 被引用 123 次
- DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and RenderingRuizhi Shao, Hongwen Zhang, He Zhang, Mingjia Chen 等CVPR 2022 · 被引用 72 次
- Accurate 3D Body Shape Regression using Metric and Semantic AttributesVasileios Choutas, Lea Müller, Chun-Hao P. Huang, Siyu Tang 等CVPR 2022 · 被引用 63 次
- Multiview Human Body Reconstruction from Uncalibrated CamerasZhixuan Yu, Linguang Zhang, Yuanlu Xu, Chengcheng Tang 等NeurIPS 2022 · 被引用 24 次
- Delving Deep into Pixel Alignment Feature for Accurate Multi-View Human Mesh RecoveryKai Jia, Hongwen Zhang, Liang An, Yebin LiuAAAI 2023 · 被引用 17 次
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