Generating Furry Cars: Disentangling Object Shape & Appearance across Multiple Domains

TL;DR

This paper introduces a method for learning disentangled representations of object shape and appearance across multiple domains, enabling the generation of novel hybrid images by interchanging these factors.

Contribution

It extends existing disentanglement techniques to handle cross-domain scenarios using a differentiable histogram of visual features for appearance representation.

Findings

Effective shape and appearance transfer across domains

Accurate disentanglement of shape and appearance factors

Generates novel images combining features from different domains

Abstract

We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e.g., dogs and cars). The goal is to learn a generative model that learns an intermediate distribution, which borrows a subset of properties from each domain, enabling the generation of images that did not exist in any domain exclusively. This challenging problem requires an accurate disentanglement of object shape, appearance, and background from each domain, so that the appearance and shape factors from the two domains can be interchanged. We augment an existing approach that can disentangle factors within a single domain but struggles to do so across domains. Our key technical contribution is to represent object appearance with a differentiable histogram of visual features, and to optimize the generator so that two images with the same latent appearance factor but different latent shape factors produce similar histograms. On multiple multi-domain datasets, we demonstrate our method leads to accurate and consistent appearance and shape transfer across domains.