CoStyler: Indoor Furniture Styling by Counterfactual Reasoning in a Hypergraph Field

Fixed-layout indoor furniture styling requires selecting assets that form a coherent room while preserving furniture categories, positions, orientations, and scales. Existing approaches typically retrieve each asset independently or rely on static local relations, making them prone to shape, material, and color conflicts after scene composition. We introduce CoStyler, a scene-level structured selection framework built on a dynamic hypergraph style field. It uses a frozen multimodal large language model to extract style priors, maintains candidate distributions for furniture slots, and captures higher-order dependencies through style-conditioned hyperedge activation and weighting. Counterfactual style preference learning evaluates candidates as local substitutions using Mahalanobis energies to measure contextual compatibility. Training alternates between optimizing the style field and candidate logits; inference freezes the model and iteratively updates only room-specific candidate logits to resolve cross-slot conflicts. Experiments on 3D-FRONT show that CoStyler outperforms object- and scene-level retrieval baselines in furniture retrieval and scene-level style coherence.