Paper Info
Title
On the Importance of Feature Aggregation for Face Reconstruction
Abstract
The goal of this work is to seek principles of designing a deep neural network for 3D face reconstruction from a single image. To make the evaluation simple, we generated a synthetic dataset and used it for evaluation. We conducted extensive experiments using an end-to-end face reconstruction algorithm using E2FAR and its variations, and analyzed the reason why it can be successfully applied for 3D face reconstruction. From the comparative studies, we conclude that feature aggregation from different layers is a key point to training better neural networks for 3D face reconstruction. Based on these observations, a face reconstruction feature aggregation network (FR-FAN) is proposed, which obtains significant improvements compared with baselines on the synthetic validation set. We evaluate our model on existing popular indoor and in-the-wild 2D-3D datasets. Extensive experiments demonstrate that FR-FAN performs 16.50% and 9.54% better than E2FAR on BU-3DFE and JNU-3D, respectively. Finally, the sensitivity analysis we performed on controlled datasets demonstrates that our designed network is robust to large variations of pose, illumination, and expressions.
Year
DOI
Venue
2019
10.1109/WACV.2019.00103
2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
Keywords
Field
DocType
Face,Shape,Three-dimensional displays,Image reconstruction,Solid modeling,Training,Two dimensional displays
Iterative reconstruction,Computer vision,Expression (mathematics),Pattern recognition,Computer science,Reconstruction algorithm,Artificial intelligence,Solid modeling,Feature aggregation,Artificial neural network
Conference
ISSN
ISBN
Citations 
2472-6737
978-1-7281-1975-5
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Xiang Xu1305.58
Ha Le221.06
Ioannis A. Kakadiaris31910203.66