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ID-Unet: Iterative-view-synthesis(CVPR2021 Oral)
Tensorflow implementation of ID-Unet: Iterative Soft and Hard Deformation for View Synthesis.
Overview architecture
<p align="center"> <img src="./results/overview.png" width="55%"><br><center></center></p>The network architecture
<p align="center"> <img src="./results/architecture.png" width="95%"><br><center></center></p> <p align="center"> <img src="./results/CDM.png" width="95%"><br><center></center></p>Experiment Results
- chair
- MultiPIE
- Flow
Preparation
- Prerequisites
- Tensorflow
- Python 2.x with matplotlib, numpy and scipy
- Dataset
- Download model
- Tool model
- model should be placed in ./models/
- Tool model
Quick Start
Exemplar commands are listed here for a quick start.
dataset
-
prepare dataset
python datasets/creat_txt.py --path_MultiPIE 'Path to MultiPIE Dataset' --path_chair 'Path to chair Dataset' --path_300w_LP 'Path to 300w-LP Dataset' shuf datasets/multiPIE_train_paired.txt -o datasets/multiPIE_train_paired_shuf.txt python datasets/creat_tf.py --path_MultiPIE 'Path to MultiPIE Dataset' --path_chair 'Path to chair Dataset' --path_300w_LP 'Path to 300w-LP Dataset'
Training
-
To train with size of 128 X 128
python MultiPIE.py --mode training python chair.py --mode training
Testing
-
Example of test
python MultiPIE.py --mode test --batch_size 1 --model_path 'Path to Training Model' python chair.py --mode test --batch_size 1 --model_path 'Path to Training Model'
Citation
If this work is useful for your research, please consider citing:
@inproceedings{yin2021id, title={ID-Unet: Iterative Soft and Hard Deformation for View Synthesis}, author={Yin, Mingyu and Sun, Li and Li, Qingli}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={7220--7229}, year={2021} }