LP${}^2$SLat: Learning Physical Priors on Structured Latents for Single-Image Generated 3D Assets

Single-image 3D generators produce textured assets, but physical simulation requires material properties that vary across object parts. We introduce LP${}^2$SLat, a dual-branch network that predicts material type, density, Young’s modulus, and Poisson’s ratio for single-image generated 3D assets. The network combines complementary information from structured 3D latents and the source image: the latent branch predicts a base physical field using local features and object context, while the image branch refines it through gated appearance-based corrections. The predicted properties are attached directly to the generated mesh, without additional 3D reconstruction or per-object optimization. For training, we transfer reference-part annotations through the shared source view, accommodating geometric differences between reference and generated assets and filtering ambiguous correspondences. Together, these components connect single-image asset generation with spatial physical-property prediction and downstream physics-based simulation.