Bridging Implicit and Explicit Geometric Transformations for Single-Image View Synthesis

Creating novel views from a single image has achieved tremendous strides with advanced autoregressive models. 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. The proposed model is an efficient non-autoregressive model with implicit and explicit renderers. Motivated by characteristics that explicit methods well preserve reprojected pixels and implicit methods complete realistic out-of-view region, 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.