  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.

Computer Vision and Pattern Recognition