Abstract
Token-level warping in vision-language models demonstrates superior stability and semantic coherence for viewpoint transformation compared to pixel-wise methods, achieving better visual reasoning performance.
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.
Community
CVPR 2026
Paper: https://arxiv.org/abs/2604.02870
Project Page: https://token-warping-mllm.github.io/
Code: https://github.com/KAIST-Visual-AI-Group/Token-Warping-MLLM
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the backward token warping idea is the core of this work, building a dense target-view grid and fetching source tokens via a lightweight 3d proxy mesh, which keeps semantics far more stable than pixel-level warping. i’d still like to see how this handles heavy occlusion or nonuniform lighting where depth error is high, because those failure modes could reveal if token-level representations truly carry robust part-level structure. the arxivlens breakdown helped me parse the method steps and the retrieval loop without getting lost in patch noise (https://arxivlens.com/PaperView/Details/token-warping-helps-mllms-look-from-nearby-viewpoints-13-2d118584). would you run an ablation on grid density versus depth noise to isolate which source of error hurts performance more?
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