  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.
