Object Centric Generative Model

Object-centric generative models aim to represent scenes as collections of individual objects, enabling more robust and generalizable AI systems. Current research focuses on developing models that can accurately segment, reconstruct, and reason about objects from various input modalities (e.g., RGB-D images, video), often employing architectures like diffusion models and transformers, and incorporating techniques like active inference for improved learning and decision-making. These advancements are significantly improving performance in tasks such as 3D scene understanding, object manipulation, and reinforcement learning, particularly in robotics and computer vision applications. The resulting structured scene representations offer a powerful alternative to traditional approaches, leading to more efficient and interpretable AI systems.