MaskGWM: A Generalizable Driving World Model with Video Mask Reconstruction

Understand

World models that forecast environmental changes from actions are vital for autonomous driving models with strong generalization.

• The prevailing driving world model mainly build on video prediction model.
• Although these models can produce high-fidelity video sequences with advanced diffusion-based generator, they are constrained by their predictive duration and overall generalization capabilities.
• In this paper, we explore to solve this problem by combining generation loss with MAE-style feature-level context learning.