Perspective-Aware Reasoning in Vision-Language Models via Mental Imagery
  Simulation
Authors: name: Phillip Y. Lee, name: Jihyeon Je, name: Chanho Park, name: Mikaela Angelina Uy, name: Leonidas Guibas, name: Minhyuk Sung

We present a framework for perspective-aware reasoning in vision-language
models (VLMs) through mental imagery simulation. Perspective-taking, the
ability to perceive an environment or situation from an alternative viewpoint,
is a key benchmark for human-level visual understanding, essential for
environmental interaction and collaboration with autonomous agents. Despite
advancements in spatial reasoning within VLMs, recent research has shown that
modern VLMs significantly lack perspective-aware reasoning capabilities and
exhibit a strong bias toward egocentric interpretations. To bridge the gap
between VLMs and human perception, we focus on the role of mental imagery,
where humans perceive the world through abstracted representations that
facilitate perspective shifts. Motivated by this, we propose a framework for
perspective-aware reasoning, named Abstract Perspective Change (APC), that
effectively leverages vision foundation models, such as object detection,
segmentation, and orientation estimation, to construct scene abstractions and
enable perspective transformations. Our experiments on synthetic and real-image
benchmarks, compared with various VLMs, demonstrate significant improvements in
perspective-aware reasoning with our framework, further outperforming
fine-tuned spatial reasoning models and novel-view-synthesis-based approaches.