4DRefGS: Reflective 4D Gaussian Splatting for Multi-View Dynamic Scene Reconstruction

Reconstructing dynamic reflective scenes from multi-view videos is challenging because physical motion and reflected appearance are strongly entangled. Our ob- servation is that surface-attached and localized virtual reflections exhibit different dynamic behaviors: the former remain tied to physical surface, while the latter may exhibit independent apparent motion. Modeling both with the same dynamic primitives can therefore entangle geometry, motion and reflected appearance. We introduce 4DRefGS, a role-aware framework for reconstructing dynamic reflec- tive scenes. First, to disentangle physical surface motion from reflection-induced apparent motion, we employ two asymmetric 4D Gaussian fields: primary planar Gaussians reconstruct the physical surface, whereas auxiliary Gaussians indepen- dently follow localized virtual-reflection cues without contributing to geometry or diffuse appearance. Second, we model reflective appearance through a reflection- aware deferred shading framework, where scene-shared directional lighting and time-varying surface-attached features predict the global reflection, while auxil- iary features provide spatially localized corrections. To facilitate evaluation, we further collect SelfCap, a synchronized multi-view dataset featuring fast motion and substantial time-varying reflections. Experiments on SelfCap and Neural3DV demonstrate that 4DRefGS faithfully reconstructs dynamic reflective appearance, substantially improving rendering quality on challenging reflective scenes while remaining competitive on general dynamic scenes.