Towards Alias-Free 4D Gaussian Representations with Motion-Aware Filtering

Novel-view synthesis of dynamic scenes, crucial for AR/VRapplications, remains a challenging problem. Recent methods adapt rep-resentations like 3D Gaussian Splatting (3DGS) and Neural RadianceFields (NeRF) for dynamic scenes by incorporating time as the fourth di-mension (4D representations). These 4D representations still suffer fromaliasing artifacts, especially when generating novel views from divergentviewpoints (zoom-in/zoom-out operations). While using 3D smoothingfilters like those proposed in Mip-Splatting might seem like a possible so-lution, they fail to account for local motion and also exhibit aliasing. Toaddress this, we propose a motion-aware 3D smoothing filter specificallydesigned for 4D representations. Our approach adapts the filter strengthbased on local motion information, effectively mitigating aliasing withoutcompromising rendering quality. This is achieved by estimating the jointdensity function of time and focal-to-depth ratio using a non-parametricestimation method. During inference, we sample from this joint distribu-tion to determine the appropriate smoothing filter. This flexible strategycan be integrated with various 4D representations. Our evaluations onstandard datasets demonstrate superior performance compared to state-of-the-art methods.