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

• URL: http://arxiv.org/abs/2603.25265v1
• Date: Thu, 26 Mar 2026 10:03:44 GMT
• Title: ViewSplat: View-Adaptive Dynamic Gaussian Splatting for Feed-Forward Synthesis
• Abstract summary: We present ViewSplat, a view-adaptive 3D Gaussian splatting network for novel view synthesis from unposed images.<n>We show that ViewSplat achieves state-of-the-art fidelity while maintaining fast inference and real-time rendering.
• Score: 10.752176613697912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present ViewSplat, a view-adaptive 3D Gaussian splatting network for novel view synthesis from unposed images. While recent feed-forward 3D Gaussian splatting has significantly accelerated 3D scene reconstruction by bypassing per-scene optimization, a fundamental fidelity gap remains. We attribute this bottleneck to the limited capacity of single-step feed-forward networks to regress static Gaussian primitives that satisfy all viewpoints. To address this limitation, we shift the paradigm from static primitive regression to view-adaptive dynamic splatting. Instead of a rigid Gaussian representation, our pipeline learns a view-adaptable latent representation. Specifically, ViewSplat initially predicts base Gaussian primitives alongside the weights of dynamic MLPs. During rendering, these MLPs take target view coordinates as input and predict view-dependent residual updates for each Gaussian attribute (i.e., 3D position, scale, rotation, opacity, and color). This mechanism, which we term view-adaptive dynamic splatting, allows each primitive to rectify initial estimation errors, effectively capturing high-fidelity appearances. Extensive experiments demonstrate that ViewSplat achieves state-of-the-art fidelity while maintaining fast inference (17 FPS) and real-time rendering (154 FPS).

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