Scene Extrapolation

Scene extrapolation, encompassing tasks like video prediction, novel view synthesis, and extending incomplete data, aims to generate plausible representations of scenes beyond observed information. Current research focuses on developing sophisticated models, including neural radiance fields (NeRFs), generative adversarial networks (GANs), diffusion models, and graph neural processes, often incorporating physics-based constraints or disentangled representations to improve accuracy and realism. These advancements have implications for diverse fields, such as medical imaging (improving diagnostic accuracy), materials science (accelerating simulations), robotics (enhancing navigation and decision-making), and virtual/augmented reality (creating immersive experiences). The ultimate goal is to create robust and efficient methods for generating high-fidelity scene extrapolations across various modalities and applications.