Generating Diverse Indoor Furniture Arrangements

TL;DR

This paper introduces a GAN-based method to generate diverse indoor furniture arrangements that vary in price and quantity, using a quality diversity algorithm to optimize the latent space for realistic and varied layouts.

Contribution

It presents a novel combination of GANs and quality diversity algorithms to produce diverse, realistic furniture arrangements with controllable attributes.

Findings

Generated arrangements resemble human-designed layouts

Arrangements vary significantly in price and number of furniture pieces

Method effectively explores diverse layout options

Abstract

We present a method for generating arrangements of indoor furniture from human-designed furniture layout data. Our method creates arrangements that target specified diversity, such as the total price of all furniture in the room and the number of pieces placed. To generate realistic furniture arrangement, we train a generative adversarial network (GAN) on human-designed layouts. To target specific diversity in the arrangements, we optimize the latent space of the GAN via a quality diversity algorithm to generate a diverse arrangement collection. Experiments show our approach discovers a set of arrangements that are similar to human-designed layouts but varies in price and number of furniture pieces.