Style2Shape: Image Style Guided 3D Shape Material Generation

We present Style2Shape, a framework for generating editable physically-based rendering (PBR) materials for a 3D mesh from a single reference image. Compared with text-guided methods, images provide high-bandwidth appearance cues but introduce two key difficulties: (i) faithfully transferring fine-grained visual style while preserving the target shape's structure, and (ii) producing physically-plausible, standard PBR assets rather than implicit, non-editable representations. To address these challenges, we propose a hybrid material representation that couples a retrieved procedural material (for physically-consistent reflectance) with a generated texture (for rich, instance-specific details), combined via learnable blending. Our pipeline consists of three stages: (1) structure-guided appearance transfer to synthesize pixel-aligned supervision, (2) hybrid initialization via physics-based retrieval and texture generation, and (3) physics-based optimization that jointly refines procedural parameters, UV transforms, and blending weights through differentiable rendering. Experiments show that Style2Shape produces high-fidelity, physically-plausible materials exported as standard PBR maps, making the results directly compatible with existing rendering workflows.