Gavin Luo

AI has made rapid progress in language and 2D visual generation, but Physical AI requires something more fundamental: the ability to represent a persistent 3D world, understand how that world changes under actions, and interact with it over time.

In this talk, VAST will share its technical evolution from 3D foundation models to world models. We will discuss how 3D generation is moving from high-fidelity visual assets toward native, pipeline-ready spatial representations, and how this spatial foundation can further evolve into persistent and interactive worlds.

A key focus will be a world-model architecture that separates world state, state transition, and generative rendering. Rather than predicting only the next video frame, the model maintains a camera-independent 3D state and learns how that state evolves, enabling long-term consistency, controllable interaction, multi-view reuse, and real-time experiences.

We will also discuss how internet video and engine-native synthetic data can be transformed into aligned 3D state and action data, providing a scalable path toward simulation and training infrastructure for Physical AI.