Episode
SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution
- Podcast
- Daily Paper Cast
- Published
- Sep 9, 2026
- Duration seconds
- 1100
- Processing state
not_requested- Canonical source
- https://share.transistor.fm/s/92fa5cfc
Actions
POST https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/scenemosaic-efficient-and-diverse-simulation-ready-scene-generation-via-hybrid-agentic-layout-evolution/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/daily-paper-cast-7079649/scenemosaic-efficient-and-diverse-simulation-ready-scene-generation-via-hybrid-agentic-layout-evolution.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
🤗 Upvotes: 26 | cs.CV Authors: Xingjian Ran, Xiaoye Mo, Sihao Liu, Jianyu Zhang, Li Luo, Bo Dai Title: SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution Arxiv: http://arxiv.org/abs/2609.05594v1 Abstract: 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 \textbf{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.