  Creating novel views from a single image has achieved tremendous strides with
advanced autoregressive models, as unseen regions have to be inferred from the
visible scene contents. Although recent methods generate high-quality novel
views, synthesizing with only one explicit or implicit 3D geometry has a
trade-off between two objectives that we call the "seesaw" problem: 1)
preserving reprojected contents and 2) completing realistic out-of-view
regions. Also, autoregressive models require a considerable computational cost.
In this paper, we propose a single-image view synthesis framework for
mitigating the seesaw problem while utilizing an efficient non-autoregressive
model. Motivated by the characteristics that explicit methods well preserve
reprojected pixels and implicit methods complete realistic out-of-view regions,
we introduce a loss function to complement two renderers. Our loss function
promotes that explicit features improve the reprojected area of implicit
features and implicit features improve the out-of-view area of explicit
features. With the proposed architecture and loss function, we can alleviate
the seesaw problem, outperforming autoregressive-based state-of-the-art methods
and generating an image $\approx$100 times faster. We validate the efficiency
and effectiveness of our method with experiments on RealEstate10K and ACID
datasets.
