View Generation

View generation focuses on creating novel perspectives of a scene from existing data, aiming to improve realism and efficiency in applications like 3D modeling, virtual reality, and medical imaging. Current research emphasizes developing robust and efficient algorithms, often leveraging neural networks such as diffusion models, neural radiance fields (NeRFs), and generative adversarial networks (GANs), to synthesize multiple consistent views from limited input (e.g., a single image or a few views). These advancements are improving the quality and speed of view generation, impacting fields ranging from robotics and virtual environments to medical diagnosis and data visualization.