Embody4D: A Generalist 4D World Model for Embodied AI

• URL: http://arxiv.org/abs/2605.01799v1
• Date: Sun, 03 May 2026 09:39:44 GMT
• Title: Embody4D: A Generalist 4D World Model for Embodied AI
• Abstract summary: Embody4D is a dedicated video-to-video world model for embodied scenarios.<n>It is capable of arbitrary novel views from a monocular video.<n>It achieves state-of-the-art performance, serving as a robust world model.
• Score: 13.488596291534243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: World models have made significant progress in modeling dynamic environments; however, most embodied world models are still restricted to 2D representations, lacking the comprehensive multi-view information essential for embodied spatial reasoning. Bridging this gap is non-trivial, primarily due to challenges from severe scarcity of paired multi-view data, the difficulty of maintaining spatiotemporal consistency in generated 3D geometries, and the tendency to hallucinate manipulation details. To address these challenges, we propose Embody4D, a dedicated video-to-video world model for embodied scenarios, capable of synthesizing arbitrary novel views from a monocular video. First, to tackle data scarcity, we introduce a 3D-aware compositional synthesis pipeline to curate a heterogeneous dataset compositing cross-embodiment robotic arms with diverse backgrounds, guaranteeing broad generalization. Second, to enforce geometric stability, we devise an adaptive noise injection strategy; by leveraging confidence disparities across image regions, this method selectively regularizes the diffusion process to ensure strict spatiotemporal consistency. Finally, to guarantee manipulation fidelity, we incorporate an interaction-aware attention mechanism that explicitly attends to the robotic interaction regions. Extensive experiments demonstrate that Embody4D achieves state-of-the-art performance, serving as a robust world model that synthesizes high-fidelity, view-consistent videos to empower downstream robotic planning and learning.

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