SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution
SceneMosaic combines image-based 3D priors with VLM agent refinement to generate diverse, simulation-ready indoor scenes 24x faster than agentic baselines.
SceneMosaic is a hybrid framework that takes an initial candidate from a learned image-to-3D prior and evolves it with VLM agents for efficiency and physical validity. It decomposes scenes into independent local units, evolves each separately, and composes the global scene via Cartesian product. On SceneEval-100 it matches the strongest agentic baseline in semantic layout quality with a 24x speedup, substantially reduces physical violations, and receives the highest human ratings. Code is publicly available.
- Hybrid design merges parametric image-to-3D priors with agentic VLM refinement
- Local-unit decomposition with Cartesian product composition enables scene diversity
- 24x speedup on SceneEval-100 with fewer physical violations
- Targets embodied AI and interactive entertainment simulation environments
- Vendors
- Hugging Face
- Products
- SceneMosaicSceneEval-100
From the original article
1 min read at huggingface.co
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.
By Xingjian Ran, Xiaoye Mo, Sihao Liu · huggingface.co
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