Novel view synthesis from a single image has been a cornerstone problem for
many Virtual Reality applications that provide immersive experiences. However,
most existing techniques can only synthesize novel views within a limited range
of camera motion or fail to generate consistent and high-quality novel views
under significant camera movement. In this work, we propose a pose-guided
diffusion model to generate a consistent long-term video of novel views from a
single image. We design an attention layer that uses epipolar lines as
constraints to facilitate the association between different viewpoints.
Experimental results on synthetic and real-world datasets demonstrate the
effectiveness of the proposed diffusion model against state-of-the-art
transformer-based and GAN-based approaches.

本文提出了一种姿态引导扩散模型来从单张图像中生成一致的、持久的新视角视频，并设计了一个利用极线作为约束的注意力层以促进不同视角之间的关联。实验结果表明，该模型在合成和真实数据集上表现优异，胜过基于 Transformer 和 GAN 的现有方法。