CSANet: Channel Spatial Attention Network for Robust 3D Face Alignment and Reconstruction

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Liu, Yilin, Guo, Xuezhou, Wang, Xinqi, Du, Fangzhou
Format: Preprint
Published: 2024
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866914816473432064
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