Massive datasets and high-capacity models have driven many recent advancements in
computer vision and natural language understanding. This work presents a platform to …

Convolutional and adversarial networks are found in various fields of knowledge and
activities. One such field is building design, a multi-disciplinary and multi-task process …

Virtual reality (VR) offers an artificial, computer generated simulation of a real life
environment. It originated in the 1960s and has evolved to provide increasing immersion …

Several RGB-D datasets have been publicized over the past few years for facilitating
research in computer vision and robotics. However, the lack of comprehensive and fine …

We present a convolutional neural network based approach for indoor scene synthesis. By
representing 3D scenes with a semantically-enriched image-based representation based on …

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 present a generative neural network that enables us to generate plausible 3D indoor
scenes in large quantities and varieties, easily and highly efficiently. Our key observation is …

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line
drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a …

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 …

Creating high‐level structured 3D models of real‐world indoor scenes from captured data is
a fundamental task which has important applications in many fields. Given the complexity …