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arXiv · 2605.06192

EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields

Zhaoyang Yang·Yurun Jin·Lizhe Qi·Cong Huang·Kai Chen
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Abstract

Pretrained video diffusion models provide powerful spatiotemporal generative priors, making them a natural foundation for robotic world models. While recent world-action models jointly optimize future videos and actions, they predominantly treat video generation as an auxiliary representation for policy learning. Consequently, they insufficiently explore the inverse problem: leveraging action signals to guide video synthesis, thereby often failing to preserve precise robot spatial geometry and fine-grained robot-object interaction dynamics in the generated rollouts. To bridge this gap, we present EA-WM, an Event-Aware Generative World Model that effectively closes the loop between kinematic control and visual perception. Rather than injecting joint or end-effector actions as abstract, low-dimensional tokens, EA-WM projects actions and kinematic states directly into the target camera view as Structured Kinematic-to-Visual Action Fields. To fully exploit this geometrically grounded representation, we introduce event-aware bidirectional fusion blocks that modulate cross-branch attention, capturing object state changes and interaction dynamics. Evaluated on the comprehensive WorldArena benchmark, EA-WM achieves state-of-the-art performance, outperforming existing baselines by a significant margin.

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Zhaoyang Yang, Yurun Jin, Lizhe Qi, Cong Huang, Kai Chen. 2026-05-07. EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields. https://arxiv.org/abs/2605.06192

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