Data for PhD Thesis: Generative Models for Multimodal 3D Scene Generation

In summary, this dissertation explores deep generative models for 3D scene synthesis from multimodal inputs. The proposed methods have broad applicability in areas such as interior design, game development, and augmented/virtual reality.

By enabling realistic 3D scene generation from a single RGB image, a structured scene graph, or a simple text prompt, our work empowers non-expert users to create tailored 3D content. As generative modeling and multimodal learning continue to evolve, these contributions offer significant potential for advancing 3D scene understanding applications.