FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation

Optical flow methods typically rely on task-specific inductivebiases, such as correlation volumes, feature warping, and iterative refine-ment, among others, to reach high accuracy. While effective, such biasesconstrain the model to predefined heuristics, which can limit its expres-sivity and lead to more complex pipelines and additional computationalcost. We present FreeFlow, a hierarchical transformer built without anyflow-specific components, using instead a single feed-forward encoder–decoder. FreeFlow combines three attention variants: window attentionfor local processing, shifted-window attention for cross-window informa-tion exchange, and a global attention operating at a reduced resolu-tion. The resulting architecture scales naturally with model capacity,enabling a consistent accuracy gain from small to large variants. Despitethe absence of standard inductive biases, FreeFlow achieves state-of-the-art results on major benchmarks, including Sintel (0.68/1.48 EPE onClean/Final), KITTI-2015 (3.23 Fl-all), and Spring (3.192 1px), whileremaining memory efficient at 1080p inference.