Generative models able to synthesize layouts of different kinds (eg documents, user
interfaces or furniture arrangements) are a useful tool to aid design processes and as a first …

We present a new framework for interior scene synthesis that combines a high-level relation
graph representation with spatial prior neural networks. We observe that prior work on scene …

We address the task of indoor scene generation by generating a sequence of objects, along
with their locations and orientations conditioned on a room layout. Large-scale indoor scene …

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 …

In this paper, we address the problem of conditional scene decoration for 360deg images.
Our method takes a 360deg background photograph of an indoor scene and generates …

3D indoor scenes are widely used in computer graphics, with applications ranging from
interior design to gaming to virtual and augmented reality. They also contain rich …

It is common in graphic design humans visually arrange various elements according to their
design intent and semantics. For example, a title text almost always appears on top of other …

People spend a large percentage of their lives indoors—in bedrooms, living rooms, kitchens,
etc. As computer graphics reproduces the real world in increasing fidelity, the demand for …

Synthesizing realistic 3D indoor scenes is a challenging task that traditionally relies on
manual arrangement and annotation by expert designers. Recent advances in …

We present a framework for fast synthesizing indoor scenes, given a room geometry and a
list of objects with learnt priors. Unlike existing data-driven solutions, which often learn priors …