| title | Scene-Designer: Learning Design Intelligence for Agentic 3D Scene Generation |
|---|---|
| authors | Xuanyu Liu, Hongyan Feng, Miao Pan, Sunlai Chen, Lizhou Liang, Bing Sun, Wenqi Zhang, Xuhong Zhang |
| date | 2026-10-16 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.23121 |
| affiliation | ZJU-OmniAI |
| upvotes | 18 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Generating complete, coherent, and usable 3D indoor scenes from natural language requires strong design intelligence: the ability to design high-quality scenes and critique their quality. However, existing approaches either rely on general-purpose models lacking domain-specific design experience or learn from static scenes. In this work, we introduce Scene-Designer, an agentic framework that learns design intelligence through complementary design and critique experiences. We construct two datasets: Scene-OTA-35K, which provides Observation--Thought--Action supervision for learning scene design and efficient error correction, and Scene-OTC-15K, which provides Observation--Thought--Critic supervision for learning scene quality critique across physical validity, functional correctness, and layout coherence. Based on these experiences, we propose Scene-GRPO, a Critic-guided GRPO framework that improves design decisions and reduces unnecessary trial-and-error corrections. Extensive experiments demonstrate that Scene-Designer achieves superior performance in physical validity, functional correctness, and layout coherence, while the learned Scene-Critic significantly outperforms general-purpose models in scene critique. Moreover, test-time Critic guidance further improves scene generation, showing that the Critic can provide useful guidance beyond training. Additional analysis suggests a trend of mutual enhancement between scene design and critique abilities. |
cite
@misc{liu2026scene,
title={Scene-Designer: Learning Design Intelligence for Agentic 3D Scene Generation},
author={Xuanyu Liu and Hongyan Feng and Miao Pan and Sunlai Chen and Lizhou Liang and Bing Sun and Wenqi Zhang and Xuhong Zhang},
year={2026},
eprint={2610.23121},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.23121}
}