# SceneMosaic：通过混合智能体布局进化实现高效多样的仿真就绪场景生成

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-04 08:00
- AIHOT 分数：37
- AIHOT 链接：https://aihot.news/items/cmttkjroo0cb7rofp2km3dlwa
- 原文链接：https://arxiv.org/abs/2609.05594

## AI 摘要

SceneMosaic 提出一种结合图像先验与 VLM 智能体的室内场景生成框架，兼顾效率与物理有效性。该方法将场景分解为独立局部单元分别进化，再通过笛卡尔积组合全局场景。在 SceneEval-100 上，SceneMosaic 在语义布局质量上与最强智能体基线持平，速度提升 24 倍，物理违规显著减少，并获得最高人工评分，代码已开源。

## 正文

Diverse and simulation-ready indoor scenes are essential for interactive entertainment and embodied AI, yet their scalable generation remains challenging. Recent agentic text-to-3D scene pipelines that rely on vision-language models (VLMs) can generate scenes of high fidelity but require costly iterative object placement and refinement. Another mainstream paradigm, parametric image-to-3D scene models, produces scenes efficiently from strong priors learned from 2D images but often leads to imprecise and physically invalid scenes. More importantly, both paradigms struggle to output diverse scenes for a single input, making it hard for them to reflect the dynamically changing nature of real scenes. In this paper we propose SceneMosaic, a framework that combines the merits of both paradigms. It obtains the initial candidate from the learned image-based prior, and subsequently evolves the result through VLM agents, ensuring both efficiency and physical validity. Within the evolution process, SceneMosaic exploits the locality of natural scenes and decomposes a scene into independent local units, allowing separate evolution within each unit before composing the global scene via Cartesian product. On SceneEval-100, SceneMosaic matches the strongest agentic baseline in semantic layout quality with a 24x speedup, substantially reduces physical violations, and receives the highest human ratings. Our code is publicly available at https://github.com/rxjfighting/SceneMosaic.