  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.
