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Novel view synthesis from monocular videos of dynamic scenes with unknown While camera recent poses remains advances a in fundamental 3D representations challenge such in computer as Neural vision Radiance and graphics. Fields (NeRF) scenes, and they 3D struggle Gaussian with Splatting dynamic (3DGS) content ha and ve sho typically wn promising rely on results pre-computed for static camera poses. We present 4D3R, a pose-free dynamic neural rendering framework that Our method decouples first static leverages and dynamic 3D foundational components models through for initial a tw pose o-stage and approach.