Token Warping Helps MLLMs Look from Nearby Viewpoints
This addresses a robustness issue in MLLMs for visual reasoning tasks, though it is incremental as it builds on existing token-based architectures.
The paper tackled the problem of multimodal large language models (MLLMs) being fragile to viewpoint changes by proposing token-level warping instead of pixel-wise warping, resulting in consistent outperformance over baselines on the ViewBench benchmark.
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.