Reconstructing non-rigid objects with physical plausibility remains challenging due to expensive per-scene optimization and the lack of physical supervision. ReconPhys is a feedforward framework that jointly learns physical attribute estimation and 3D Gaussian Splatting reconstruction from a single monocular video. A dual-branch architecture with a differentiable simulation-rendering loop enables self-supervised learning without ground-truth physics labels. On a large-scale synthetic benchmark, ReconPhys reaches 21.64 PSNR in future prediction versus 13.27 from optimization baselines, and reduces Chamfer Distance from 0.349 to 0.004 while running in under one second.
