Multilinear Autoencoder for 3D Face Model Learning

By Victoria Fernández Abrevaya, Stefanie Wuhrer, Edmond Boyer. 2018

49 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Michael J. Black
2. Luc Van Gool
3. Philip H. S. Torr
4. Cordelia Schmid
5. Ming-Hsuan Yang
6. Jitendra Malik
7. Anurag Ranjan
8. Andrew Zisserman

Citations in the corpus

• Generating 3D faces using Convolutional Mesh Autoencoders (2018, methodology): “[18] propose an autoencoder with a CNN-based encoder and a multilinear model as a decoder.”
• 3D Morphable Face Models—Past, Present, and Future (2019, unknown): “Recently, a number of methods have been proposed to address this limitation using an optimization approach [Bolkart and Wuhrer 2016], a custom tensor decomposition method [Wang et al. 2017], and an autoencoder structure [Fernán-dez Abrevaya et al. 2018], respectively.”
• 3D Hand Shape and Pose From Images in the Wild (2019, background): “While most works fix these decoders, some propose to tune them after a supervised initialization [2, 22, 55].”