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FlowGrounded-VideoPrediction

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The source code of ECCV18 'Flow-Grounded Spatial-Temporal Video Prediction from Still Images'.

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- DTexture.lua
- collectinfo.py
- data_process.sh
- generateMeta.lua
- video2frame.lua
- cloud0.png
- cloud_pred16.gif
- flag0.png
- flag_pred16.gif
- walk0.png
- walk_pred16.gif
- - init.txt
- content_conv4_1.lua
- flow_conv4_1.lua
- invert_conv4_1_concat.lua
- - fullModel2.lua
  - fullModel2_1.lua
  - modelL1_4.t7
  - modelL2_4.t7
  - modelL3_4.t7
  - modelL4_4.t7
  - modelL5_4.t7
- 1.jpg
- demo.lua
- EPECriterion.lua
- flowExtensions.lua
- image-075_128x128.png
- image-076_128x128.png
- transforms.lua
- content_loss.lua
- descriptor_net.lua
- Gaussian.lua
- InstanceNormalization.lua
- VAE.lua
c1.png
download_models.sh
gif.py
k1.jpg
README.md
test.lua
train_3DcVAE.lua
train_flow2rgb.lua
utils.lua

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# FlowGrounded-VideoPrediction
Torch implementation of our ECCV18 [paper](https://arxiv.org/pdf/1807.09755.pdf) on video prediction based on one single still image.

<p>
    <img src='examples/walk0.png' width=100 />
    <img src='examples/walk_pred16.gif' width=100 />
    <img src='examples/flag0.png' width=100 />
    <img src='examples/flag_pred16.gif' width=100 />
    <img src='examples/cloud0.png' width=100 />
    <img src='examples/cloud_pred16.gif' width=100 />
</p>

In each panel from left to right: one single starting frame and the predicted sequence (next 16 frames).

## Getting started

- Linux
- NVIDIA GPU
- Torch 
- [SPyNet](https://github.com/anuragranj/end2end-spynet)
- [ffmpeg](https://www.ffmpeg.org/)

```
git clone https://github.com/Yijunmaverick/FlowGrounded-VideoPrediction
cd FlowGrounded-VideoPrediction
```

## Preparation

- Data

- Put the video data (e.g., `.mp4` or `.avi`) in a folder and put it under `./datasets/DTexture/raw/`.
  - Run the following command to convert videos to frames and generate the metadata for training. The testing data are prepared in the same way. Make sure that the meta data for both training and testing are ready before experiments.

```
cd datasets/
sh data_process.sh
cd ..
```

- SPyNet

- We use the flows estimated by the great work [SPyNet](https://arxiv.org/abs/1611.00850) as the ground truth for training. Make sure that the SPyNet [code](https://github.com/anuragranj/end2end-spynet) is complied successfully and works well.

- Pretrained models

- Run the following command to download the pretrained VGG (for perceptual loss) and our models learned on the `KTH` and  `WavingFlag` data for testing.

```
sh download_models.sh
```

## Training

- Train the `3DcVAE` model for flow prediction:

```
th train_3DcVAE.lua --dataRoot datasets/DTexture
```
  - Train the `flow2rgb` model for frame generation:

```
th train_flow2rgb.lua --dataRoot datasets/DTexture
```

## Testing

- Test two steps (prediction + generation) together:

```
th test.lua --dataRoot datasets/DTexture
```
  - With `ffmpeg` installed, run the following command to convert the predicted frames to a gif or video:

```
python gif.py
```

## Citation

```
@inproceedings{Prediction-ECCV-2018,
    author = {Li, Yijun and Fang, Chen and Yang, Jimei and Wang, Zhaowen and Lu, Xin and Yang, Ming-Hsuan},
    title = {Flow-Grounded Spatial-Temporal Video Prediction from Still Images},
    booktitle = {European Conference on Computer Vision},
    year = {2018}
}
```

## Acknowledgement

- Codes are heavily borrowed from [DrNet](https://github.com/edenton/drnet).