Novel View Synthesis and Image Generation Using Machine Learning
Introduction
Machine learning has revolutionized the way we generate and manipulate images. One area of growing importance is novel view synthesis (NVS), which allows the generation of images from new perspectives of a given scene using limited input data. This technique plays a crucial role in computer vision, virtual reality (VR), augmented reality (AR), and 3D rendering, offering photorealistic imagery from unseen viewpoints.
Similarly, image generation models, especially those powered by Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, have enabled the creation of synthetic data for diverse applications, such as game design, advertising, and content creation. This article explores both novel view synthesis and image generation, compares techniques, and discusses their applications, benefits, and challenges.
What Is Novel View Synthesis?
Novel view synthesis refers to the task of generating new views or perspectives of a scene using a limited set of input images. NVS aims to extrapolate or interpolate between the given viewpoints to predict what the scene would look like from previously unseen angles.
