ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video

Reconstructing non-rigid objects with physical plausibilityremains a significant challenge. Existing approaches leverage differen-tiable rendering for per-scene optimization, recovering geometry anddynamics but requiring expensive tuning or manual annotation, whichlimits practicality and generalizability. To address this, we propose Re-conPhys, the first feedforward framework that jointly learns physicalattribute estimation and 3D Gaussian Splatting reconstruction from asingle monocular video. Our method employs a dual-branch architecturetrained via a self-supervised strategy, eliminating the need for ground-truth physics labels. Given a video sequence, ReconPhys simultaneouslyinfers geometry, appearance, and physical attributes. Experiments ona large-scale synthetic dataset demonstrate superior performance: ourmethod achieves 21.64 PSNR in future prediction compared to 13.27by state-of-the-art optimization baselines, while reducing Chamfer Dis-tance from 0.349 to 0.004. Crucially, ReconPhys enables fast inference(<1 second) versus hours required by existing methods, facilitating rapidgeneration of simulation-ready assets for robotics and graphics. The codeis available at https://github.com/chuanshuogushi/ReconPhys.