Multimodal Language Models Cannot Spot Spatial Inconsistencies
Spatial consistency is a fundamental property of the visual world and a key requirement for models that aim to understand physical reality. Despite recent advances, multimodal large language models (MLLMs) often struggle to reason about 3D geometry across multiple views. Rather than asking models to describe scene attributes, we introduce a more challenging task: given two views of the same scene, identify the object that violates 3D motion consistency. We propose a simple and scalable method for generating realistic, spatially inconsistent image pairs from multi-view scenes, enabling systematic evaluation of this capability. Our results show that state-of-the-art MLLMs significantly unde...
| Authors | Om Khangaonkar, Hadi J. Rad, Hamed Pirsiavash |
|---|---|
| Primary category | cs.CV |
| Published | 1 April 2026 |
| Last updated at source | 2 April 2026 |
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