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

• URL: http://arxiv.org/abs/2609.11486v1
• Date: Thu, 10 Sep 2026 12:55:04 GMT
• Title: FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation
• Abstract summary: FreeFlow is a hierarchical transformer built without any flow-specific components, using instead a single feed-forward encoder--decoder.<n>We present FreeFlow, a hierarchical transformer built without any flow-specific components, using instead a single feed-forward encoder--decoder.
• Score: 39.56619111519502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical flow methods typically rely on task-specific inductive biases, such as correlation volumes, feature warping, and iterative refinement, among others, to reach high accuracy. While effective, such biases constrain the model to predefined heuristics, which can limit its expressivity and lead to more complex pipelines and additional computational cost. We present FreeFlow, a hierarchical transformer built without any flow-specific components, using instead a single feed-forward encoder--decoder. FreeFlow combines three attention variants: window attention for local processing, shifted-window attention for cross-window information exchange, and a global attention operating at a reduced resolution. The resulting architecture scales naturally with model capacity, enabling a consistent accuracy gain from small to large variants. Despite the absence of standard inductive biases, FreeFlow achieves state-of-the-art results on major benchmarks, including Sintel (0.68/1.48 EPE on Clean/Final), KITTI-2015 (3.23 Fl-all), and Spring (3.192 1px), while remaining memory efficient at 1080p inference.

Related papers

• FlowIt: Global Matching for Optical Flow with Confidence-Guided Refinement [44.07406912385784]
  FlowIt is a novel architecture for optical flow estimation.<n>It is designed to robustly handle large pixel displacements.<n>It achieves state-of-the-art results on the competitive Sintel and KITTI benchmarks.
  arXiv  Detail & Related papers  (2026-03-30T17:58:12Z)
• PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator [73.80050807279461]
  Piecewise Rectified Flow (PeRFlow) is a flow-based method for accelerating diffusion models. PeRFlow achieves superior performance in a few-step generation.
  arXiv  Detail & Related papers  (2024-05-13T07:10:53Z)
• Boundary-aware Decoupled Flow Networks for Realistic Extreme Rescaling [49.215957313126324]
  Recently developed generative methods, including invertible rescaling network (IRN) based and generative adversarial network (GAN) based methods, have demonstrated exceptional performance in image rescaling. However, IRN-based methods tend to produce over-smoothed results, while GAN-based methods easily generate fake details. We propose Boundary-aware Decoupled Flow Networks (BDFlow) to generate realistic and visually pleasing results.
  arXiv  Detail & Related papers  (2024-05-05T14:05:33Z)
• Guided Flows for Generative Modeling and Decision Making [55.42634941614435]
  We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text synthesis-to-speech. Notably, we are first to apply flow models for plan generation in the offline reinforcement learning setting ax speedup in compared to diffusion models.
  arXiv  Detail & Related papers  (2023-11-22T15:07:59Z)
• GAFlow: Incorporating Gaussian Attention into Optical Flow [62.646389181507764]
  We push Gaussian Attention (GA) into the optical flow models to accentuate local properties during representation learning. We introduce a novel Gaussian-Constrained Layer (GCL) which can be easily plugged into existing Transformer blocks. For reliable motion analysis, we provide a new Gaussian-Guided Attention Module (GGAM)
  arXiv  Detail & Related papers  (2023-09-28T07:46:01Z)
• Kernelised Normalising Flows [10.31916245015817]
  Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation. We present Ferumal flow, a novel kernelised normalising flow paradigm that integrates kernels into the framework.
  arXiv  Detail & Related papers  (2023-07-27T13:18:52Z)
• AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation [17.501820140334328]
  We introduce AnyFlow, a robust network that estimates accurate flow from images of various resolutions. We establish a new state-of-the-art performance of cross-dataset generalization on the KITTI dataset.
  arXiv  Detail & Related papers  (2023-03-29T07:03:51Z)
• GMFlow: Learning Optical Flow via Global Matching [124.57850500778277]
  We propose a GMFlow framework for learning optical flow estimation. It consists of three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for global feature matching, and a self-attention layer for flow propagation. Our new framework outperforms 32-iteration RAFT's performance on the challenging Sintel benchmark.
  arXiv  Detail & Related papers  (2021-11-26T18:59:56Z)
• FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation [81.76975488010213]
  Dense optical flow estimation plays a key role in many robotic vision tasks. Current networks often occupy large number of parameters and require heavy computation costs. Our proposed FastFlowNet works in the well-known coarse-to-fine manner with following innovations.
  arXiv  Detail & Related papers  (2021-03-08T03:09:37Z)
• Self Normalizing Flows [65.73510214694987]
  We propose a flexible framework for training normalizing flows by replacing expensive terms in the gradient by learned approximate inverses at each layer. This reduces the computational complexity of each layer's exact update from $mathcalO(D3)$ to $mathcalO(D2)$. We show experimentally that such models are remarkably stable and optimize to similar data likelihood values as their exact gradient counterparts.
  arXiv  Detail & Related papers  (2020-11-14T09:51:51Z)
• OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport [8.468007443062751]
  A normalizing flow is an invertible mapping between an arbitrary probability distribution and a standard normal distribution. OT-Flow tackles two critical computational challenges that limit a more widespread use of CNFs. On five high-dimensional density estimation and generative modeling tasks, OT-Flow performs competitively to state-of-the-art CNFs.
  arXiv  Detail & Related papers  (2020-05-29T22:31:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.