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Physically Compatible 3D Object Modeling from a Single Image
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We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal attributes: mechanical properties, external forces, and rest-shape geometry. Existing single-view 3D reconstruction methods often overlook this underlying composition, presuming rigidity or neglecting external forces. Consequently, the reconstructed objects fail to withstand real-world physical forces, resulting in instability or undesirable deformation -- diverging from their intended designs as depicted in the image. Our optimization framework addresses this by embedding physical compatibility into the reconstruction process. We explicitly decompose the three physical attributes and link them through static equilibrium, which serves as a hard constraint, ensuring that the optimized physical shapes exhibit desired physical behaviors. Evaluations on a dataset collected from Objaverse demonstrate that our framework consistently enhances the physical realism of 3D models over existing methods. The utility of our framework extends to practical applications in dynamic simulations and 3D printing, where adherence to physical compatibility is paramount.
Forward citations
Cited by 5 Pith papers
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PhysX-CoT: Structured Physical Reasoning from a Single Image to Simulation-Ready 3D Assets
PhysX-CoT turns single-image 3D asset generation into an explicit, ordered, supervised chain of physical states, beating an output-centric VLM baseline on geometry and physical attributes.
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MIND: Microstructure INverse Design with Generative Hybrid Neural Representation
MIND trains a latent diffusion model on a physics-aware 2D plane representation of microstructures to generate diverse, tileable structures whose elastic properties match target values with state-of-the-art accuracy.
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Generative Physical AI in Vision: A Survey
A structured review that categorizes physics-aware generative models in vision into explicit-simulation and implicit-learning families and proposes six integration paradigms.
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PhyCAGE: Physically Plausible Compositional 3D Asset Generation from a Single Image
A single-image pipeline that generates physically plausible compositional 3D Gaussian Splatting assets by using a physics simulator as a gradient-driven optimizer.
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Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels
A supervised 3D U-Net predicts per-voxel material fields from CLIP feature grids, enabling fast MPM-based animation, but the reported evidence depends on pseudo-labels and a VLM judge from the same model family as the...
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