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.

翻译：我们从人设计的家具布局数据中提出室内家具安排的方法。我们的方法创建了针对特定多样性的安排,例如室内所有家具的总价格和放置的件数。为了产生现实的家具安排,我们用人设计的布局来培训一个基因对抗网络(GAN)。为了在安排中针对特定的多样性,我们通过质量多样性算法优化GAN的潜在空间,以产生多样化安排的集合。实验显示,我们的方法发现了一套与人设计的布局相似但家具价格和数量不同的安排。