LA30 / FlowDiffuser

PyTorch implementation of FlowDiffuser: Advancing Optical Flow Estimation with Diffusion Models (CVPR-2024)

[CVPR 2024] FlowDiffuser: Advancing Optical Flow Estimation with Diffusion Models

Ao Luo^1,2, Xin Li³, Fan Fang³, Jiangyu Liu², Haoqiang Fan², and Shuaicheng Liu^4,2

1. Southwest Jiaotong University   2. Megvii Research   3.Group 42

4. University of Electronic Science and Technology of China

This project provides the official implementation of 'FlowDiffuser: Advancing Optical Flow Estimation with Diffusion Models'.

Abstract

Optical flow estimation, a process of predicting pixel-wise displacement between consecutive frames, has commonly been approached as a regression task in the age of deep learning. Despite notable advancements, this de facto paradigm unfortunately falls short in generalization performance when trained on synthetic or constrained data. Pioneering a paradigm shift, we reformulate optical flow estimation as a conditional flow generation challenge, unveiling FlowDiffuser — a new family of optical flow models that could have stronger learning and generalization capabilities. FlowDiffuser estimates optical flow through a ‘noise-to-flow’ strategy, progressively eliminating noise from randomly generated flows conditioned on the provided pairs. To optimize accuracy and efficiency, our FlowDiffuser incorporates a novel Conditional Recurrent Denoising Decoder (Conditional-RDD), streamlining the flow estimation process. It incorporates a unique Hidden State Denoising (HSD) paradigm, effectively leveraging the information from previous time steps. Moreover, FlowDiffuser can be easily integrated into existing flow networks, leading to significant improvements in performance metrics compared to conventional implementations. Experiments on challenging benchmarks, including Sintel and KITTI, demonstrate the effectiveness of our FlowDiffuser with superior performance to existing state-of-the-art models.

Overview

Requirements

Python 3.8 with following packages

pytorch  1.9.0
torchvision  0.10.0
numpy  1.19.5
opencv-python  4.6.0.66
timm  0.6.12
scipy  1.5.4
matplotlib  3.3.4

Usage

1. Download Sintel and KITTI dataset, and set the root path of each class in ./core/datasets.py.

2. Put *.pth file (GoogleDrive) into folder ./weights.

3. Evaluation on Sintel and KITTI

./eval.sh

Q & A

Due to some changes in my job, I am busy with other matters. If you have any questions, please email me at [e-mail]. I will respond to you at my earliest convenience.

Citation

If you think this work is helpful, please cite

@inproceedings{luo2024flowdiffuser,
  title={FlowDiffuser: Advancing Optical Flow Estimation with Diffusion Models},
  author={Luo, Ao and Li, Xin and Yang, Fan and Liu, Jiangyu and Fan, Haoqiang and Liu, Shuaicheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={19167--19176},
  year={2024}
}

Acknowledgement

The code is built based on RAFT, SKFlow, DiffusionDet, and EMD-Flow. We thank the authors for their contributions.