Token Warping Helps MLLMs Look from Nearby Viewpoints

TL;DR

This paper introduces token warping techniques for multimodal large language models, demonstrating that backward token warping enhances viewpoint change robustness and improves scene understanding from nearby perspectives.

Contribution

It proposes a novel token warping method, especially backward warping, that outperforms pixel-wise warping and other baselines in viewpoint reasoning tasks.

Findings

Backward token warping outperforms pixel-wise warping.

Token warping improves viewpoint robustness in MLLMs.

Experiments on ViewBench show superior reasoning accuracy.

Abstract

Can warping tokens, rather than pixels, help multimodal large language models (MLLMs) understand how a scene appears from a nearby viewpoint? While MLLMs perform well on visual reasoning, they remain fragile to viewpoint changes, as pixel-wise warping is highly sensitive to small depth errors and often introduces geometric distortions. Drawing on theories of mental imagery that posit part-level structural representations as the basis for human perspective transformation, we examine whether image tokens in ViT-based MLLMs serve as an effective substrate for viewpoint changes. We compare forward and backward warping, finding that backward token warping, which defines a dense grid on the target view and retrieves a corresponding source-view token for each grid point, achieves greater stability and better preserves semantic coherence under viewpoint shifts. Experiments on our proposed ViewBench benchmark demonstrate that token-level warping enables MLLMs to reason reliably from nearby viewpoints, consistently outperforming all baselines including pixel-wise warping approaches, spatially fine-tuned MLLMs, and a generative warping method.