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AFLW2K3D
3D Keypoints
Face
|...
License: 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}
}
Data Summary
Type
Image,
Amount
--
Size
4.82GB
Provided by
Institute of Automation, Chinese Academy of Sciences
Institute of Automation, Chinese Academy of Sciences was established in October 1956, is the earliest establishment of the National Automated research institutions.
| Amount -- | Size 4.82GB
AFLW2K3D
3D Keypoints
Face
License: 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}
}
0
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