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arXiv · 2605.29563

Planning with the Views

Kangrui Wang·Linjie Li·Zhengyuan Yang·Shiqi Chen·Zihan Wang·Li Fei-Fei·Jiajun Wu·Leonidas Guibas·Lijuan Wang·Manling Li
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Abstract

Can VLMs predict how each camera move changes the view, and plan many such moves ahead? We call this capability view planning, requiring (1)understanding how a single action transforms the view, and (2)composing many such transformations across multi-turn plans to identify a target view. We probe both abilities in our proposed ViewSuite, a 3D point-cloud environment on real ScanNet scenes. Across 13 frontier VLMs, a critical planning gap emerges: they possess basic view-action knowledge but fail to compose it across multi-turn plans, with the gap widening as viewpoint distance grows. To close this gap, we propose an iterative framework that alternates self-exploration with view graph distillation. The key insight is that all exploration trajectories, regardless of their outcome, collectively form a view graph that compactly captures how viewpoints connect across a scene. Distilling this graph into diverse supervised tasks reshapes the policy distribution and overcomes the sparse rewards that stall pure RL. This improves Qwen2.5-VL-7B from 2.5% to 47.8% on interactive view planning, surpassing GPT-5.4 Pro (18.5%) and Gemini 3.1 Pro (21.4%). Self-exploration emerges as a promising path toward VLMs that can actively reason and plan in 3D space. Code and Data are at https://viewsuite.github.io.

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Kangrui Wang, Linjie Li, Zhengyuan Yang, Shiqi Chen, Zihan Wang, Li Fei-Fei, Jiajun Wu, Leonidas Guibas, Lijuan Wang, Manling Li. 2026-05-28. Planning with the Views. https://arxiv.org/abs/2605.29563

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