Bronstein^1,2,4^ Stefanos Zafeiriou^1,3^\
    Imperial College London, UK^1^ Universita Svizzera Italiana, Switzerland^2^ FaceSoft.io^3^ Twitter^4^\
    ^1^[{g.bouritsas18, s.ploumpis, m.bronstein, s.zafeiriou}@imperial.ac.uk]{} ^2^[bokhns@usi.ch]{}
bibliography:
- 'egbib.bib'
title: 'Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation'
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![image](architecture_figure.png){width="\linewidth"}

Introduction
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The success of deep learning in computer vision and image analysis, speech recognition, and natural language processing, has driven the recent interest in developing similar models for 3D geometric data. Generalisations of successful architectures such as convolutional neural networks (CNNs) to data with non-Euclidean structure (e.g. manifolds and graphs) is known under the umbrella term [*Geometric deep learning*]{} [@bronstein2017geometric]. In applications dealing with 3D data, the key challenge of geometric deep learning is a meaningful definition of intrinsic operations analogous to convolution and pooling on meshes or point clouds. Among numerous advantages of working directly on mesh or point cloud data is the fact that it is possible to build invariance to shape transformations (both rigid and nonrigid) into the architecture, as a result allowing to use significantly simpler models and much less training data. So far, the main focus of research in the field of geometric deep learning has been on [*analysis*]{} tasks, encompassing shape classification and segmentation [@qi2017pointnet; 