Spatial Mental Modeling from Limited Views
Authors: name: Baiqiao Yin, name: Qineng Wang, name: Pingyue Zhang, name: Jianshu Zhang, name: Kangrui Wang, name: Zihan Wang, name: Jieyu Zhang, name: Keshigeyan Chandrasegaran, name: Han Liu, name: Ranjay Krishna, name: Saining Xie, name: Manling Li, name: Jiajun Wu, name: Li Fei-Fei

Can Vision Language Models (VLMs) imagine the full scene from just a few
views, like humans do? Humans form spatial mental models, internal
representations of unseen space, to reason about layout, perspective, and
motion. Our new MindCube benchmark with 21,154 questions across 3,268 images
exposes this critical gap, where existing VLMs exhibit near-random performance.
Using MindCube, we systematically evaluate how well VLMs build robust spatial
mental models through representing positions (cognitive mapping), orientations
(perspective-taking), and dynamics (mental simulation for "what-if" movements).
We then explore three approaches to help VLMs approximate spatial mental
models, including unseen intermediate views, natural language reasoning chains,
and cognitive maps. The significant improvement comes from a synergistic
approach, "map-then-reason", that jointly trains the model to first generate a
cognitive map and then reason upon it. By training models to reason over these
internal maps, we boosted accuracy from 37.8% to 60.8% (+23.0%). Adding
reinforcement learning pushed performance even further to 70.7% (+32.9%). Our
key insight is that such scaffolding of spatial mental models, actively
constructing and utilizing internal structured spatial representations with
flexible reasoning processes, significantly improves understanding of
unobservable space.