In this work, we present SceneDreamer, an unconditional generative model for unbounded
3D scenes, which synthesizes large-scale 3D landscapes from random noise. Our …

Automatically generating high-quality real world 3D scenes is of enormous interest for
applications such as virtual reality and robotics simulation. Towards this goal, we introduce …

We introduce GAUDI, a generative model capable of capturing the distribution of complex
and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle …

Despite increasingly realistic image quality, recent 3D image generative models often
operate on 3D volumes of fixed extent with limited camera motions. We investigate the task …

Generating large-scale 3D scenes cannot simply apply existing 3D object synthesis
technique since 3D scenes usually hold complex spatial configurations and consist of a …

Abstract State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to
parameterize 3D radiance fields. While demonstrating impressive results, querying an MLP …

We present DreamCraft3D, a hierarchical 3D content generation method that produces high-
fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image …

Existing 3D-aware image synthesis approaches mainly focus on generating a single
canonical object and show limited capacity in composing a complex scene containing a …

We present simultaneous generation and mapping (SGAM), a novel 3D scene generation
algorithm. Our goal is to produce a realistic, globally consistent 3D world on a large scale …

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 …