Code & Data

 

Human face modeling

  • Multilinear autoencoders (WACV 2018): multilinear model learned from a large database with missing and noisy data efficiently using a novel autoencoder architecture that fixes the decoder to a tensor-based model and code to use this model.
  • Robust multilinear model learning framework for 3D faces (CVPR 2016): code to learn a multilinear model of high quality from a database with missing data, partial data, and erroneous correspondence information. On this page, the optimized face models can also be downloaded.
  • Multilinear correspondence optimization for 3D faces (ICCV 2015): code to jointly optimize a multilinear model and the registration of the 3D scans used for training. On this page, the optimized face models can also be downloaded.
  • Statistical 3D face models (ECCV 2014, CVIU 2014, CVIU 2015): four high-quality statistical 3D shape models of human faces along with code to fit the models to noisy input scans.

Human body modeling

3D shape modeling