IMAGIREP paper: depth and top-down maps beat RGB when world models imagine unseen scenes
mohitban47 · x · 2026-10-28
The paper "Representing the Unseen: Rethinking Visual Imagination for Spatial Reasoning" asks how world models should represent imagined environments for agents.
- Existing work leans on generated future RGB views, but even a ground-truth imagined RGB view can hurt accuracy on farthest-object questions, while depth or a top-down map of the same imagined state improves performance.
- The question isn't whether to imagine, but how to represent what is imagined.
- IMAGIREP is a controlled benchmark fixing the imagined state and question while varying the representation (RGB, depth, normals, panorama, top-down, text, video). Different questions benefit from different representations, and the benefit persists even when the state is estimated.
Related event: Paper: Depth and Top-Down Views Beat RGB for Spatial Reasoning(3 posts)→
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