AFLW2K3D
3D Keypoints
Face
|...
许可协议: Unknown

Overview

We propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{zhu2016face,
  title={Face alignment across large poses: A 3d solution},
  author={Zhu, Xiangyu and Lei, Zhen and Liu, Xiaoming and Shi, Hailin and Li, Stan Z},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={146--155},
  year={2016}
}
数据概要
数据格式
Image,
数据量
--
文件大小
4.9GB
发布方
Institute of Automation, Chinese Academy of Sciences
The Institute of Automation, Chinese Academy of Sciences is a research lab belonging to the Chinese Academy of Sciences which researches robotics, pattern recognition and control theory.
数据集反馈
相似数据集
AFLW
Face
立即开始构建AI