Abstract: Autonomous furniture arrangement plays an important role in many computer vision and graphics applications,such as indoor scene design and dynamic scene generation.The traditional approaches leverage the common spatial,semantical and functional object-object relationships to understand the inner structure of the indoor scene and facilitate the scene generation task.Benefited from the large-scale indoor scene dataset,we propose to embed the unstructured furniture into the graph structure,and leverage the graph neural network to iteratively learn the distribution of scene layout.In order to satisfy the diversity of furniture arrangement,we propose to incorporate the graph neural network into a conditional variational autoencoder.It leverages an encoder to input the scene information into a latent vector that represent the Gaussian distribution,and employ a generator to decode the scene layout from the sampled Gaussian noise for conditional new scene generation.Experimentally,we observe better quality of our algorithm compared to various baselines via the minimum matching distance on the public Fu-floor benchmark.The proposed algorithm is important for many practical applications,including scene completion,interior design based on scene graph and so on.