Scenethesis: A Language and Vision Agentic Framework for 3D Scene
  Generation
Authors: name: Lu Ling, name: Chen-Hsuan Lin, name: Tsung-Yi Lin, name: Yifan Ding, name: Yu Zeng, name: Yichen Sheng, name: Yunhao Ge, name: Ming-Yu Liu, name: Aniket Bera, name: Zhaoshuo Li

Synthesizing interactive 3D scenes from text is essential for gaming, virtual
reality, and embodied AI. However, existing methods face several challenges.
Learning-based approaches depend on small-scale indoor datasets, limiting the
scene diversity and layout complexity. While large language models (LLMs) can
leverage diverse text-domain knowledge, they struggle with spatial realism,
often producing unnatural object placements that fail to respect common sense.
Our key insight is that vision perception can bridge this gap by providing
realistic spatial guidance that LLMs lack. To this end, we introduce
Scenethesis, a training-free agentic framework that integrates LLM-based scene
planning with vision-guided layout refinement. Given a text prompt, Scenethesis
first employs an LLM to draft a coarse layout. A vision module then refines it
by generating an image guidance and extracting scene structure to capture
inter-object relations. Next, an optimization module iteratively enforces
accurate pose alignment and physical plausibility, preventing artifacts like
object penetration and instability. Finally, a judge module verifies spatial
coherence. Comprehensive experiments show that Scenethesis generates diverse,
realistic, and physically plausible 3D interactive scenes, making it valuable
for virtual content creation, simulation environments, and embodied AI
research.