Abstract: We address the problem of view synthesis in complex outdoor scenes. We propose a novel convolutional neural network architecture that includes flow-based and direct synthesis sub-networks. Both sub-networks introduce novel elements that greatly improve the quality of the synthesized images. These images are then adaptively fused to create the final output image. Our approach achieves state-of-the-art performance on the KITTI dataset, which is commonly used to evaluate view-synthesis methods. Unlike many recently proposed methods, ours is trained without the need for additional geometric constraints, such as a ground-truth depth map, making it more broadly applicable. Our approach also achieved the best performance on the Brooklyn Panorama Synthesis dataset, which we introduce as a new, challenging benchmark for view synthesis. Our dataset, code, and pretrained models are available at url{https://mvrl.github.io/GAF}.