Geometry-Aware Rotary Position Embedding for Consistent Video World Model

Xiangchendong, JEdward, Jintao Zhang, Xiao Yang, zhengwei fang, Shizun Wang, Zijun Wang, Yingtian Zou, Hang Su, Jun Zhu

Geometry-Aware Rotary Position Embedding for Consistent Video World Model: 11 upvotes on Hugging Face Daily Papers, #10 of 25 papers on 2026-02-18. Day-by-day upvote history.

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 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 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 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.

Paper page on Hugging Face · arXiv

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