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

StyleForge addresses furniture styling under a fixed room layout, where furniture categories, positions, orientations, and scales cannot change. A frozen multimodal language model extracts structured style priors from the user request and layout. A dynamic hypergraph style field models higher-order dependencies among furniture slots, while counterfactual preference learning evaluates each candidate as a local substitution using Mahalanobis energies. During inference, only room-specific candidate logits are updated through test-time training. The abstract reports state-of-the-art furniture retrieval and improved scene-level style coherence on 3D-FRONT, but provides no quantitative results.