Learning to Estimate Hidden Motions with Global Motion Aggregation
This repository contains the source code for our paper:
Learning to Estimate Hidden Motions with Global Motion Aggregation
ICCV 2021
Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, Richard Hartley
ANU, Oxford
You will have to choose cudatoolkit version to match your compute environment. The code is tested on PyTorch 1.8.0 but other versions might also work.
conda create --name gma python==3.7
conda activate gma
conda install pytorch=1.8.0 torchvision=0.9.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install matplotlib imageio einops scipy opencv-pythonsh demo.shsh train.shsh evaluate.shWTFPL. See LICENSE file.
The overall code framework is adapted from RAFT. We thank the authors for the contribution. We also thank Phil Wang for open-sourcing transformer implementations.