LVLMs can be trained to proactively imagine scenes from task-relevant viewpoints by providing explicit supervision through generated spatial reasoning trajectories and perspective-taking tasks, significantly improving performance on spatial reasoning benchmarks.
This paper introduces SpatialASM, a method to teach Large Vision-Language Models to actively imagine scenes from different viewpoints when solving spatial reasoning tasks.