LAYOUTDREAMER: Physics-guided Layout for Text-to-3D Compositional Scene Generation
This addresses the challenge of generating high-quality, physically consistent 3D scenes for applications like virtual reality or gaming, though it appears incremental as it builds on existing 3D Gaussian Splatting methods.
The paper tackles the problem of generating physically plausible and controllable 3D scenes from text by introducing LayoutDreamer, which uses 3D Gaussian Splatting and scene graphs to improve layout and realism, achieving state-of-the-art performance on the T3Bench metric for multiple objects generation.
Recently, the field of text-guided 3D scene generation has garnered significant attention. High-quality generation that aligns with physical realism and high controllability is crucial for practical 3D scene applications. However, existing methods face fundamental limitations: (i) difficulty capturing complex relationships between multiple objects described in the text, (ii) inability to generate physically plausible scene layouts, and (iii) lack of controllability and extensibility in compositional scenes. In this paper, we introduce LayoutDreamer, a framework that leverages 3D Gaussian Splatting (3DGS) to facilitate high-quality, physically consistent compositional scene generation guided by text. Specifically, given a text prompt, we convert it into a directed scene graph and adaptively adjust the density and layout of the initial compositional 3D Gaussians. Subsequently, dynamic camera adjustments are made based on the training focal point to ensure entity-level generation quality. Finally, by extracting directed dependencies from the scene graph, we tailor physical and layout energy to ensure both realism and flexibility. Comprehensive experiments demonstrate that LayoutDreamer outperforms other compositional scene generation quality and semantic alignment methods. Specifically, it achieves state-of-the-art (SOTA) performance in the multiple objects generation metric of T3Bench.