Zero Shot Novel View

Zero-shot novel view synthesis (NVS) aims to generate realistic images of a scene from unseen viewpoints, using only a single or a few reference images without requiring model training on specific datasets. Current research heavily utilizes pre-trained diffusion models, often combined with techniques like monocular depth estimation and multi-view attention mechanisms, to achieve this. This capability is crucial for applications such as autonomous driving, robotics, and 3D object reconstruction, offering significant advancements in computer vision and related fields by enabling more robust and data-efficient 3D scene understanding. The focus is on improving the fidelity, consistency, and controllability of generated views, particularly for complex scenes and objects.