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

• URL: http://arxiv.org/abs/2605.06192v1
• Date: Thu, 07 May 2026 13:06:19 GMT
• Title: EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields
• Abstract summary: We present EA-WM, an EventAware Generative World Model that closes the loop between kinematic control and visual perception.<n>We introduce event-aware bidirectional fusion blocks that modulate cross-branch attention, capturing object state changes and precise interaction dynamics.<n>EA-WM achieves state-of-the-art performance, outperforming existing baselines by a significant margin.
• Score: 15.319293934673915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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