RGB-D video

WorldString: Actionable World Representation via Neural Architecture for Object State Modeling

This paper proposes WorldString, a neural architecture designed to model the state manifold of real-world objects by learning from point clouds or RGB-D video streams. Unlike prior approaches that rely on video generation or dynamic scene reconstruction, WorldString explicitly models object action states in a unified, principled framework. It is positioned as a foundational building block for physical world models, functioning as a versatile digital twin. Its fully differentiable structure is intended to enable integration with policy learning and neural dynamics.