3D Gaussian splatting enables high-quality novel view synthesis (NVS) at real-time frame rates. However, its quality drops sharply as we depart from the training views. Thus, very dense captures involving many images are needed to match the high-quality expectations of some applications, e.g. Virtual Reality (VR). However, dense captures are very laborious and expensive to obtain. Existing works have explored using 2D generative models to alleviate this requirement by distillation or generating additional training views. These methods are often conditioned only on a handful of reference input views and thus do not fully exploit the available 3D information, leading to inconsistent generation results and reconstruction artifacts. To tackle this problem, we propose a multi-view, flow-matching model that learns a flow to connect novel views generated from possibly-sparse reconstructions to renderings that we expect from dense reconstructions. This enables augmenting scene captures with generated novel views to improve the overall reconstruction quality.Our model is trained on a novel dataset of 3.6M image pairs and can process up to 45 views at 540x960 resolution (91K tokens) on one H100 GPU in a single forward pass. Our pipeline consistently improves NVS in few-view and many-view scenarios, leading to higher-quality reconstructions than prior works across multiple, widely-used NVS benchmarks.