Hash-guided Diffusion: Efficient Generation

Physics-based simulation of sewing patterns yields globally correct but overly smooth garment surfaces, and recovering high-frequency detail such as wrinkles and seams with optimization- or diffusion-based refinement typically requires hundreds to thousands of iterations. We present Hash-guided Diffusion (HD), which keeps the simulated surface fixed and generates only a residual displacement field over a frozen UV parameterization, a mapping of the 3D surface onto 2D coordinate charts obtained with Nuvo, a neural UV parameterization. The residual is represented by a multiresolution hash encoding for constant-time lookup of local high-frequency detail, and is generated by a conditional diffusion process with either a discrete (DDPM) or a deterministic probability-flow ODE reverse solver. On a representative stiff garment from GarmentCodeData v2, HD-ODE with 10 reverse steps attains lower panel-level and image-level error than 500-step discrete diffusion (Panel L2 4.72 vs. 5.18 cm). Performance depends strongly on material stiffness, with the best results on stiff fabrics. These results indicate that a geometry-aligned residual domain can substantially shorten diffusion-based surface refinement.