Recent years have witnessed remarkable advances in artificial intelligence generated
content (AIGC), with diverse input modalities, eg, text, image, video, audio and 3D. The 3D is …

Text-driven 3D indoor scene generation is useful for gaming, the film industry, and AR/VR
applications. However, existing methods cannot faithfully capture the room layout, nor do …

We present BlockFusion, a diffusion-based model that generates 3D scenes as unit blocks
and seamlessly incorporates new blocks to extend the scene. BlockFusion is trained using …

We present Frankenstein, a diffusion-based framework that can generate semantic-
compositional 3D scenes in a single pass. Unlike existing methods that output a single …

We present a method for generating Streetscapes—long sequences of views through an on-
the-fly synthesized city-scale scene. Our generation is conditioned by language input (eg …

There is increased interest in using generative AI to create 3D spaces for Virtual Reality (VR)
applications. However, today's models produce artificial environments, falling short of …

Complex 3D scene understanding has gained increasing attention, with scene encoding
strategies playing a crucial role in this success. However, the optimal scene encoding …

Creating large-scale interactive 3D environments is essential for the development of
Robotics and Embodied AI research. However, generating diverse embodied environments …

We present a system for generating indoor scenes in response to text prompts. The prompts
are not limited to a fixed vocabulary of scene descriptions, and the objects in generated …

Recent advances in text-to-image diffusion models have significantly enhanced image
generation quality, when trained on internet-scale data. However, existing methods are …