3D Scene Diffusion Guidance using Scene Graphs
Mohammad Naanaa Katharina Schmid Yinyu Nie
Guided synthesis of high-quality 3D scenes is a challenging task. Diffusion
models have shown promise in generating diverse data, including 3D scenes.
However, current methods rely directly on text embeddings for controlling the
generation, limiting the incorporation of complex spatial relationships between
objects. We propose a novel approach for 3D scene diffusion guidance using
scene graphs. To leverage the relative spatial information the scene graphs
provide, we make use of relational graph convolutional blocks within our
denoising network. We show that our approach significantly improves the
alignment between scene description and generated scene.