PhysConvex: Physics-Informed Dynamic Convex Fields for Reconstruction and Simulation

Reconstructing deformable objects from video requires a 4Drepresentation that should preserve geometry, explain motion throughphysics, and generalize to future or new physical conditions. Existingdynamic NeRF and Gaussian methods achieve strong view synthesis,yet their voxel or ellipsoidal primitives are primarily designed for ap-pearance rendering and typically driven by centers or predefined par-ticle bindings, limiting physically meaningful non-uniform deformationand sharp boundary evolution. We present PhysConvex, a physics-informed dynamic convex field for video-based reconstruction, physicalsystem identification, and simulation. PhysConvex represents a dynamicobject as material-space deformable convex primitives whose boundaryis advected by physical dynamics. The boundary-driven convex expressesnon-uniform deformation and evolving active supports while serving si-multaneously as a rendering element, deformation carrier, and physicalsupport where mass, elastic response, forces, and contacts are evalu-ated. We further introduce a mesh-free reduced-order convex simulatorin which neural skinning modes define physics-based deformation basesdirectly over deformable convex supports. Experiments show improveddynamic and physical reconstruction, efficiency, future prediction, andgeneralization to changed materials, forces, and boundary conditions.