Novel View Image

Novel view image synthesis aims to generate realistic images from viewpoints not present in the original dataset, a crucial task in computer vision with applications ranging from medical imaging to virtual reality. Current research heavily utilizes neural radiance fields (NeRFs) and diffusion models, often incorporating techniques like geometry priors, multi-view consistency constraints, and cascading refinement stages to improve image quality and handle sparse or low-resolution input data. These advancements address limitations in traditional methods, particularly for dynamic scenes and scenarios with limited input views, leading to more robust and accurate novel view generation. The resulting improvements have significant implications for fields requiring 3D scene reconstruction and manipulation from limited visual data.