CSANet: Channel Spatial Attention Network for Robust 3D Face Alignment and Reconstruction
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914816473432064 |
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| author | Liu, Yilin Guo, Xuezhou Wang, Xinqi Du, Fangzhou |
| author_facet | Liu, Yilin Guo, Xuezhou Wang, Xinqi Du, Fangzhou |
| contents | Our project proposes an end-to-end 3D face alignment and reconstruction network. The backbone of our model is built by Bottle-Neck structure via Depth-wise Separable Convolution. We integrate Coordinate Attention mechanism and Spatial Group-wise Enhancement to extract more representative features. For more stable training process and better convergence, we jointly use Wing loss and the Weighted Parameter Distance Cost to learn parameters for 3D Morphable model and 3D vertices. Our proposed model outperforms all baseline models both quantitatively and qualitatively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19659 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CSANet: Channel Spatial Attention Network for Robust 3D Face Alignment and Reconstruction Liu, Yilin Guo, Xuezhou Wang, Xinqi Du, Fangzhou Computer Vision and Pattern Recognition Image and Video Processing Our project proposes an end-to-end 3D face alignment and reconstruction network. The backbone of our model is built by Bottle-Neck structure via Depth-wise Separable Convolution. We integrate Coordinate Attention mechanism and Spatial Group-wise Enhancement to extract more representative features. For more stable training process and better convergence, we jointly use Wing loss and the Weighted Parameter Distance Cost to learn parameters for 3D Morphable model and 3D vertices. Our proposed model outperforms all baseline models both quantitatively and qualitatively. |
| title | CSANet: Channel Spatial Attention Network for Robust 3D Face Alignment and Reconstruction |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2405.19659 |