ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models

Vision-language models (VLMs) have demonstrated remark-able capabilities in understanding and reasoning about visual content,but significant challenges persist in tasks requiring cross-viewpoint under-standing and spatial reasoning. We identify a critical limitation: currentVLMs excel primarily at egocentric spatial reasoning (from the cam-era’s perspective) but fail to generalize to allocentric viewpoints whenrequired to adopt another entity’s spatial frame of reference. We introduceViewSpatial-Bench, a dedicated multi-perspective spatial localizationbenchmark covering both camera-perspective and human perspective rea-soning, supported by an automated 3D annotation pipeline that generatesprecise directional labels. Comprehensive evaluation of diverse VLMs onViewSpatial-Bench reveals a significant performance disparity: modelsdemonstrate reasonable performance on camera-perspective tasks butexhibit reduced accuracy when reasoning from a human viewpoint. Byfine-tuning VLMs on our multi-perspective spatial dataset, we achievean overall performance improvement of 46.24% across tasks, confirmingthe strong latent spatial reasoning capacity of VLMs under targetedsupervision. Our work establishes a crucial benchmark for spatial intel-ligence in embodied AI systems and provides empirical evidence thatmodeling 3D spatial relationships enhances VLMs’ corresponding spatialcomprehension capabilities.Our benchmark is available at HuggingFace.