:sparkles:Paper :sparkles:Poster :sparkles:Presentation (YouTube) :sparkles:Slide :sparkles:OpenReview
Pytorch 1.8.1 & CUDA 10.1.
Please referring to requirements.txt for details.
If your CUDA version is 10.1, you can directly execute the following command to install the environment:
conda create -n scandmm python==3.7
conda activate scandmm
pip install -r requirements.txt
python train.py --seed=1234 --dataset='./Datasets/Sitzmann.pkl' --lr=0.0003 --bs=64 --epochs=500 --save_root='./model/'
python inference.py --model='./model/model_lr-0.0003_bs-64_epoch-435.pkl' --inDir='./demo/input' --outDir='./demo/output' --n_scanpaths=200 --length=20 --if_plot=True
sp_P48_5376x2688.png
scanpaths = np.load(P48_5376x2688.npy)
print(scanpaths.shape)
(200, 20, 2)
# (n_scanpaths, length, (y, x)). (y, x) are normalized coordinates in the range [0, 1] (y/x = 0 indicate the top/left edge).
sp_P8_7500x3750.png
scanpaths = np.load(P8_7500x3750.npy)
print(scanpaths.shape)
(200, 20, 2)
@InProceedings{scandmm2023,
title={ScanDMM: A Deep Markov Model of Scanpath Prediction for 360° Images},
author={Xiangjie Sui and Yuming Fang and Hanwei Zhu and Shiqi Wang and Zhou Wang},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
year={2023}
}
The author would like to thank Kede Ma for his inspiration, Daniel Martin et al. for publishing ScanGAN model and visualization functions, and Bingham Eli et al. for the implementation of Pyro. We sincerely appreciate for their contributions.