SceneCast: Learning Indoor Scene Synthesis from Curated Synthetic Videos with LLM Reward

3D indoor scene synthesis requires generating objects to form a complete and coherent scene. Existing approaches rely heavily on manually annotated datasets, leading to high costs and limited generalization. We present \ours, an annotation-free framework that trains on large-scale, unlabeled videos, addressing two key challenges: (1) Videos provide only partial views of a scene, requiring the model to extrapolate unseen 3D regions, which is inherently error-prone; (2) Raw videos available online are uncurated, suffering from uncontrolled conditions and variable data quality. First, we design a Transformer-based model that predicts objects sequentially and is fine-tuned with reinforcement learning (RL), where an LLM acts as a spatial common-sense evaluator. The LLM provides reward signals through chain-of-thought reasoning and code-executable geometric checks to refine object configurations. Second, we introduce \trim, a data curation approach that generates training videos with controllable video generation models. Crucially, it retains only high-quality prefix clips via multi-criteria quality assessment with an entropy-based weighting strategy. Evaluated on curated synthetic video sets, our annotation-free approach outperforms state-of-the-art supervised methods in scene quality.