Conditional 360-degree Image Synthesis for Immersive Indoor Scene Decoration

TL;DR

This paper introduces a novel method for conditional indoor scene decoration in 360-degree images, enabling diverse, controllable, and realistic furniture arrangements with state-of-the-art results and immersive quality.

Contribution

We develop a 360-aware object layout generator and a cyclic scene emptier to improve controllability and realism in 360-degree indoor scene synthesis.

Findings

Achieves state-of-the-art performance on Structure3D dataset

Generates diverse and controllable furniture layouts

Generalizes well to Zillow indoor scene dataset

Abstract

In this paper, we address the problem of conditional scene decoration for 360-degree images. Our method takes a 360-degree background photograph of an indoor scene and generates decorated images of the same scene in the panorama view. To do this, we develop a 360-aware object layout generator that learns latent object vectors in the 360-degree view to enable a variety of furniture arrangements for an input 360-degree background image. We use this object layout to condition a generative adversarial network to synthesize images of an input scene. To further reinforce the generation capability of our model, we develop a simple yet effective scene emptier that removes the generated furniture and produces an emptied scene for our model to learn a cyclic constraint. We train the model on the Structure3D dataset and show that our model can generate diverse decorations with controllable object layout. Our method achieves state-of-the-art performance on the Structure3D dataset and generalizes well to the Zillow indoor scene dataset. Our user study confirms the immersive experiences provided by the realistic image quality and furniture layout in our generation results. Our implementation will be made available.