Int3Rcept: Physics-Driven Structural Redesign via 3D Generative Intermediate Representations

Modern 3D generative models synthesize visually compelling assets, but physics-driven redesign must support substantial structural changes without sacrificing visual quality. Existing approaches struggle to achieve both: learned-feature optimization preserves generative priors but provides only indirect control over structural changes, while explicit grid-based optimization offers direct spatial control but operates outside the native synthesis space of pretrained generators. We introduce Int3Rcept, a physics-driven 3D redesign framework that intercepts and redesigns a pretrained generator's intermediate spatial representation under physical objectives. Our key idea is to use a spatially explicit representation as a direct design space while remaining compatible with downstream synthesis. Int3Rcept expands the intermediate structure into a volumetric design domain, optimizes a continuous density field under volume constraints, and converts it back through connectivity-aware discretization, followed by downstream synthesis. Int3Rcept achieves stronger physical improvement than learned-feature optimization and higher visual quality than explicit grid-based optimization. It also shows strong controllability across target material volumes, enabling support pruning, reinforcement, and opening creation. We further demonstrate the interface across multiple physical objectives and pretrained 3D generators. Together, these results establish intermediate spatial representations as a practical bridge between physical optimization and 3D generative priors.