We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

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 …

Holistic 3D scene understanding entails estimation of both layout configuration and object
geometry in a 3D environment. Recent works have shown advances in 3D scene estimation …

3D scene synthesis facilitates and benefits many real-world applications. Most scene
generators focus on making indoor scenes plausible via learning from training data and …

Deep generative models have been used in recent years to learn coherent latent
representations in order to synthesize high-quality images. In this work, we propose a neural …

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 …

This book surveys current and future approaches to generating video game content with
machine learning or Procedural Content Generation via Machine Learning (PCGML) …

Automated floorplanning or space layout planning has been a long-standing NP-hard
problem in the field of computer-aided design, with applications in integrated circuits …

Spatial computing experiences are constrained by the real-world surroundings of the user.
In such experiences, augmenting virtual objects to existing scenes require a contextual …