[NeurIPS' 24] StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences

Paper

TODO

• Clean Code
• Pip Package Support
• Training & Inference Code
• Pre-trained Weights

Environment Setup

sh install.sh

(Flash-attention is optional, supporting faster inference and less GPU memory. The implementation of StreamFlow with flash-attention support is in test_memory.py.)

Checkpoints Preparation

Download twins_svt_large-90f6aaa9.pth and put it into the pretrained dir. This checkpoint is used in training.

Checkpoints on FlyingThings, Sintel, KITTI, and Spring could be downloaded from here.

Usage

The inference pipeline for an input video and the visualization on the predicted flows are in demo.py.

Training Data Preparation

├── datasets
    ├── Sintel
        ├── test
        ├── training
    ├── KITTI
        ├── testing
        ├── training
        ├── devkit
    ├── FlyingChairs_release
        ├── data
    ├── FlyingThings3D
        ├── frames_cleanpass
        ├── frames_finalpass
        ├── optical_flow
    ├── Spring
        ├── train

Training

# Stage 1. On Things
sh scripts/train_things.sh 

# Stage 2. On Sintel / KITTI
sh scripts/train_sintel_kitti.sh

# Stage 3. On Spring
sh scripts/train_spring.sh

Inference on Specific Datasets

sh scripts/infer.sh

Acknowledgement

Parts of code are adapted from the following repositories. We thank the authors for their great contribution to the community:

• RAFT
• SKFlow
• MemFlow