Geometry-Aware Rotary Position Embedding for Consistent Video World Model

• URL: http://arxiv.org/abs/2602.07854v2
• Date: Tue, 17 Feb 2026 06:42:24 GMT
• Title: Geometry-Aware Rotary Position Embedding for Consistent Video World Model
• Authors: Chendong Xiang, Jiajun Liu, Jintao Zhang, Xiao Yang, Zhengwei Fang, Shizun Wang, Zijun Wang, Yingtian Zou, Hang Su, Jun Zhu,
• Abstract summary: ViewRope is a geometry-aware encoding that injects camera-ray directions directly into video transformer self-attention layers.<n>Geometry-Aware Frame-Sparse Attention exploits these geometric cues to selectively attend to relevant historical frames.<n>Our results demonstrate that ViewRope substantially improves long-term consistency while reducing computational costs.
• Score: 48.914346802616414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predictive world models that simulate future observations under explicit camera control are fundamental to interactive AI. Despite rapid advances, current systems lack spatial persistence: they fail to maintain stable scene structures over long trajectories, frequently hallucinating details when cameras revisit previously observed locations. We identify that this geometric drift stems from reliance on screen-space positional embeddings, which conflict with the projective geometry required for 3D consistency. We introduce \textbf{ViewRope}, a geometry-aware encoding that injects camera-ray directions directly into video transformer self-attention layers. By parameterizing attention with relative ray geometry rather than pixel locality, ViewRope provides a model-native inductive bias for retrieving 3D-consistent content across temporal gaps. We further propose \textbf{Geometry-Aware Frame-Sparse Attention}, which exploits these geometric cues to selectively attend to relevant historical frames, improving efficiency without sacrificing memory consistency. We also present \textbf{ViewBench}, a diagnostic suite measuring loop-closure fidelity and geometric drift. Our results demonstrate that ViewRope substantially improves long-term consistency while reducing computational costs.

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