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 …

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

To understand and analyze human behavior, we need to capture humans moving in, and
interacting with, the world. Most existing methods perform 3D human pose estimation …

Humans live within a 3D space and constantly interact with it to perform tasks. Such
interactions involve physical contact between surfaces that is semantically meaningful. Our …

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 …

Next generation task-oriented dialog systems need to understand conversational contexts
with their perceived surroundings, to effectively help users in the real-world multimodal …

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 …

This report surveys advances in deep learning‐based modelling techniques that address
four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We …

We introduce a learning framework for automated floorplan generation which combines
generative modeling using deep neural networks and user-in-the-loop designs to enable …