Consistent View Synthesis with Bidirectional Epipolar Attention and Reconstruction

Novel view synthesis from a single image aims to generate novel scene views given a reference image and a sequence of camera poses. Its primary difficulty lies in effectively leveraging a generative model to achieve high-quality image generation while simultaneously ensuring consistency and faithfulness across synthesized views. In this paper, we propose a novel approach to address the consistency and faithfulness issues in view synthesis. Specifically, we develop a new attention layer, termed bidirectional epipolar attention, which utilizes a pair of complementary epipolar lines to guide the associations between features from different viewpoints. Each bidirectional epipolar layer calculates forward and backward epipolar lines, enabling geometrically constrained attention that improves cross-view consistency. To ensure faithful synthesis, we introduce an epipolar-aware reconstruction module that prevents creating novel content in regions where the newly generated image overlaps with existing ones. Extensive experimental results demonstrate that our method outperforms previous approaches to novel view synthesis, achieving superior performance in both image quality and consistency. The source code is available at https://github.com/fallantbell/Bidirectional-Epipolar-Synthesis.