InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior

• URL: http://arxiv.org/abs/2402.04717v1
• Date: Wed, 7 Feb 2024 10:09:00 GMT
• Title: InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior
• Authors: Chenguo Lin, Yadong Mu
• Abstract summary: InstructScene is a novel generative framework that integrates a semantic graph prior and a layout decoder. We show that the proposed method surpasses existing state-of-the-art approaches by a large margin.
• Score: 27.773451301040424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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