ViewSplat: View-Adaptive Dynamic Gaussian Splatting for Feed-Forward Synthesis

We present ViewSplat, a view-adaptive 3D Gaussian splat-ting network for novel view synthesis from unposed images. While recentfeed-forward 3D Gaussian splatting has significantly accelerated 3D scenereconstruction by bypassing per-scene optimization, a fundamental fidelitygap remains. We attribute this gap to the limited capacity of single-stepfeed-forward networks to regress static Gaussian primitives that sat-isfy all viewpoints. To address this limitation, we shift the paradigmfrom static primitive regression to view-adaptive splatting. Instead of arigid Gaussian representation, our pipeline learns a view-adaptive latentrepresentation. Specifically, ViewSplat initially predicts base Gaussianprimitives alongside the weights of scene-conditioned View MLPs. Duringrendering, these MLPs take target-view coordinates as input and predictview-dependent residual updates for each Gaussian attribute (i.e., 3Dposition, scale, rotation, opacity, and color). This mechanism, whichwe term view-adaptive splatting, allows each primitive to rectify initialestimation errors, effectively capturing high-fidelity appearances. Exten-sive experiments demonstrate that ViewSplat achieves state-of-the-artfidelity while maintaining fast inference and real-time rendering; our largebackbone variant runs at 15 FPS during inference and 90 FPS duringrendering.