We propose a novel framework which we call mesh variational autoencoders, to explore the probabilistic latent space of 3D surfaces. The framework is easy to train, and requires very few training examples. We also propose an extended model which allows flexibly adjusting the significance of different latent variables by altering the prior distribution.
2018,  CVPR 2018.

We propose to combine the identification of key temporal and spatial regions and sign language classification into one deep learning network architecture, using attention mechanisms to focus on effective features, and realizing end-to-end automatic sign language recognition. Our proposed architecture can enhance the efficiency of sign language recognition and maintain a comparable recognition accuracy with state-of-art work.
2018,  Bachelor Thesis.

We propose a novel mesh-based autoencoder architecture that is able to cope with meshes with irregular topology. We introduce sparse regularization in this framework, which along with convolutional operations, helps localize mesh deformations. Our framework is capable of extracting localized deformation components from mesh data sets with large-scale deformations and is robust to noise.
2018,  AAAI-18 (Spotlight).