  Comprehending natural language instructions is a charming property for 3D
indoor scene synthesis systems. Existing methods directly model object joint
distributions and express object relations implicitly within a scene, thereby
hindering the controllability of generation. We introduce InstructScene, a
novel generative framework that integrates a semantic graph prior and a layout
decoder to improve controllability and fidelity for 3D scene synthesis. The
proposed semantic graph prior jointly learns scene appearances and layout
distributions, exhibiting versatility across various downstream tasks in a
zero-shot manner. To facilitate the benchmarking for text-driven 3D scene
synthesis, we curate a high-quality dataset of scene-instruction pairs with
large language and multimodal models. Extensive experimental results reveal
that the proposed method surpasses existing state-of-the-art approaches by a
large margin. Thorough ablation studies confirm the efficacy of crucial design
components. Project page: https://chenguolin.github.io/projects/InstructScene.
