Neural Implicit Style-net: synthesizing shapes in a preferred style exploiting self supervision

We introduce a novel approach to disentangle style from content in the 3D domain and perform unsupervised neural style transfer.Our approach is able to extract style information from 3D input in a self supervised fashion, conditioning the definition of style on inductive biases enforced explicitly, in the form of specific augmentations applied to the input.This allows, at test time, to select specifically the features to be transferred between two arbitrary 3D shapes, being still able to capture complex changes (e.g. combinations of arbitrary geometrical and topological transformations) with the data prior. Coupled with the choice of representing 3D shapes as neural implicit fields, we are able to perform style transfer in a controllable way, handling a variety of transformations.We validate our approach qualitatively and quantitatively on a dataset with font style labels.