# Unsupervised Part-Based Disentangling of Object Shape and Appearance

## TL;DR

This paper introduces an unsupervised, part-based model that disentangles object shape and appearance across various categories, enabling improved pose prediction and image synthesis without requiring annotations.

## Contribution

It presents a novel unsupervised method for learning consistent object parts that disentangle shape and appearance, applicable to arbitrary object categories without prior annotations.

## Key findings

- Outperforms state-of-the-art in unsupervised keypoint prediction
- Achieves competitive results in shape and appearance transfer
- Effective across diverse object categories and tasks

## Abstract

Large intra-class variation is the result of changes in multiple object characteristics. Images, however, only show the superposition of different variable factors such as appearance or shape. Therefore, learning to disentangle and represent these different characteristics poses a great challenge, especially in the unsupervised case. Moreover, large object articulation calls for a flexible part-based model. We present an unsupervised approach for disentangling appearance and shape by learning parts consistently over all instances of a category. Our model for learning an object representation is trained by simultaneously exploiting invariance and equivariance constraints between synthetically transformed images. Since no part annotation or prior information on an object class is required, the approach is applicable to arbitrary classes. We evaluate our approach on a wide range of object categories and diverse tasks including pose prediction, disentangled image synthesis, and video-to-video translation. The approach outperforms the state-of-the-art on unsupervised keypoint prediction and compares favorably even against supervised approaches on the task of shape and appearance transfer.