Deep Gradual-Conversion and Cycle Network for Single-View Synthesis

Abstract

With the popular application of convolutional neural networks in computational intelligence, research on deep learning-based view synthesis has been a hot topic. Although promising performance has been achieved by the existing learning-based view synthesis methods, how to obtain a clearer target view in the single-view synthesis task is still a challenging problem. In this paper, we propose a novel deep gradual-conversion and cycle network (DGCC-Net) for single-view synthesis by jointly considering the gradual and cycle synthesis between source and target views. Specifically, a gradual conversion mechanism is designed to synthesize a clearer target view in a gradual manner, which learns the progressive rotation trend from the source to the target view by introducing the intermediate transformation. Based on the synthesized target view, a cycle synthesis mechanism is designed to further promote the learning of single-view synthesis network by mapping the synthesized target back to the source view. By utilizing the proposed gradual conversion and cycle synthesis mechanisms, the whole network achieves a cycle view synthesis mapping between source and target views to obtain a better target view. Experiments on widely used datasets indicate the proposed DGCC-Net exceeds state-of-the-art methods.