  We present a new, fast and flexible pipeline for indoor scene synthesis that
is based on deep convolutional generative models. Our method operates on a
top-down image-based representation, and inserts objects iteratively into the
scene by predicting their category, location, orientation and size with
separate neural network modules. Our pipeline naturally supports automatic
completion of partial scenes, as well as synthesis of complete scenes. Our
method is significantly faster than the previous image-based method and
generates result that outperforms it and other state-of-the-art deep generative
scene models in terms of faithfulness to training data and perceived visual
quality.
