Pretraining on large labeled datasets is a prerequisite to achieve good performance in many
computer vision tasks like image recognition, video understanding etc. However, pretraining …

Generating realistic 3D worlds occupied by moving humans has many applications in
games, architecture, and synthetic data creation. But generating such scenes is expensive …

Abstract We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a
new, large-scale, and compre-hensive repository of synthetic indoor scenes highlighted by …

Humans universally dislike the task of cleaning up a messy room. If machines were to help
us with this task, they must understand human criteria for regular arrangements, such as …

The ability to synthesize realistic and diverse indoor furniture layouts automatically or based
on partial input, unlocks many applications, from better interactive 3D tools to data synthesis …

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 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 …

Inspired by cognitive theories, we introduce AnyHome, a framework that translates any text
into well-structured and textured indoor scenes at a house-scale. By prompting Large …

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 …

Virtual Reality (VR) is becoming ubiquitous with the rise of consumer displays and
commercial VR platforms. Such displays require low latency and high quality rendering of …