vid2vid

Project | YouTube | Paper | arXiv

Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic video-to-video translation. It can be used for turning semantic label maps into photo-realistic videos, synthesizing people talking from edge maps, or generating human motions from poses.

Video-to-Video Synthesis
Ting-Chun Wang¹, Ming-Yu Liu¹, Jun-Yan Zhu², Guilin Liu¹, Andrew Tao¹, Jan Kautz¹, Bryan Catanzaro¹
¹NVIDIA Corporation, ²MIT CSAIL In arXiv, 2018.

Video-to-Video Translation

Label-to-Streetview Results

Edge-to-Face Results

Pose-to-Body Results

Prerequisites

Linux or macOS
Python 3
NVIDIA GPU + CUDA cuDNN

Getting Started

Installation

Install PyTorch and its dependencies from https://pytorch.org
Install python libraries dominate and requests.

pip install dominate requests

Clone this repo:

git clone https://github.com/NVIDIA/vid2vid
cd vid2vid

Testing

We include an example Cityscapes video in the datasets folder.
First, download and compile a snapshot of the FlowNet2 repo from https://github.com/NVIDIA/flownet2-pytorch by running python scripts/download_flownet2.py.
Please download the pre-trained Cityscapes model by:
```
python scripts/download_models.py
```
To test the model (bash ./scripts/test_2048.sh):
```
#!./scripts/test_2048.sh
python test.py --name label2city_2048 --loadSize 2048 --n_scales_spatial 3 --use_instance --fg --use_single_G
```
The test results will be saved to a HTML file here: ./results/label2city_2048/test_latest/index.html.
We also provide a smaller model trained with single GPU, which produces slightly worse performance at 1024 x 512 resolution.
- Please download the model by
```
python scripts/download_models_g1.py
```
- To test the model (bash ./scripts/test_1024_g1.sh):
```
#!./scripts/test_1024_g1.sh
python test.py --name label2city_1024_g1 --loadSize 1024 --n_scales_spatial 3 --use_instance --fg --n_downsample_G 2 --use_single_G
```
You can find more example scripts in the scripts directory.

Dataset

We use the Cityscapes dataset as an example. To train a model on the full dataset, please download it from the official website (registration required).
We apply a pre-trained segmentation algorithm to get the corresponding semantic maps (train_A) and instance maps (train_inst).
Please add the obtained images to the datasets folder in the same way the example images are provided.

Training

First, download the FlowNet2 checkpoint file by running python scripts/download_models_flownet2.py.

Training with 8 GPUs:

We adopt a coarse-to-fine approach, sequentially increasing the resolution from 512 x 256, 1024 x 512, to 2048 x 1024.
Train a model at 512 x 256 resolution (bash ./scripts/train_512.sh)

#!./scripts/train_512.sh
python train.py --name label2city_512 --gpu_ids 0,1,2,3,4,5,6,7 --n_gpus_gen 6 --n_frames_total 6 --use_instance --fg

Train a model at 1024 x 512 resolution (must train 512 x 256 first) (bash ./scripts/train_1024.sh):

#!./scripts/train_1024.sh
python train.py --name label2city_1024 --loadSize 1024 --n_scales_spatial 2 --num_D 3 --gpu_ids 0,1,2,3,4,5,6,7 --n_gpus_gen 4 --use_instance --fg --niter_step 2 --niter_fix_global 10 --load_pretrain checkpoints/label2city_512

To view training results, please checkout intermediate results in ./checkpoints/label2city_1024/web/index.html. If you have TensorFlow installed, you can see TensorBoard logs in ./checkpoints/label2city_1024/logs by adding --tf_log to the training scripts.
Training with a single GPU:
- We trained our models using multiple GPUs. For convenience, we provide some sample training scripts (XXX_g1.sh) for single GPU users, up to 1024 x 512 resolution. Again a coarse-to-fine approach is adopted (256 x 128, 512 x 256, 1024 x 512). Performance is not guaranteed using these scripts.
- For example, to train a 256 x 128 video with a single GPU (bash ./scripts/train_256_g1.sh)
```
#!./scripts/train_256_g1.sh
python train.py --name label2city_256_g1 --loadSize 256 --use_instance --fg --n_downsample_G 2 --num_D 1 --max_frames_per_gpu 6 --n_frames_total 6
```

Training at full (2k x 1k) resolution

To train the images at full resolution (2048 x 1024) requires 8 GPUs with at least 24G memory (bash ./scripts/train_2048.sh). If only GPUs with 12G/16G memory are available, please use the script ./scripts/train_2048_crop.sh, which will crop the images during training. Performance is not guaranteed with this script.

Training with your own dataset

If your input is a label map, please generate label maps which are one-channel whose pixel values correspond to the object labels (i.e. 0,1,...,N-1, where N is the number of labels). This is because we need to generate one-hot vectors from the label maps. Please use --label_nc N during both training and testing.
If your input is not a label map, please specify --label_nc 0 and --input_nc N where N is the number of input channels (The default is 3 for RGB images).
The default setting for preprocessing is scaleWidth, which will scale the width of all training images to opt.loadSize (1024) while keeping the aspect ratio. If you want a different setting, please change it by using the --resize_or_crop option. For example, scaleWidth_and_crop first resizes the image to have width opt.loadSize and then does random cropping of size (opt.fineSize, opt.fineSize). crop skips the resizing step and only performs random cropping. scaledCrop crops the image while retraining the original aspect ratio. If you don't want any preprocessing, please specify none, which will do nothing other than making sure the image is divisible by 32.

More Training/Test Details

The way we train the model is as follows: suppose we have 8 GPUs, 4 for generators and 4 for discriminators, and we want to train 28 frames. Also, assume each GPU can generate only one frame. The first GPU generates the first frame, and pass it to the next GPU, and so on. After the 4 frames are generated, they are passed to the 4 discriminator GPUs to compute the losses. Then the last generated frame becomes input to the next batch, and the next 4 frames in the training sequence are loaded into GPUs. This is repeated 7 times (4 x 7 = 28), to train all the 28 frames.
Some important flags:
- n_gpus_gen: the number of GPUs to use for generators (while the others are used for discriminators). We separate generators and discriminators into different GPUs since when dealing with high resolutions, even one frame cannot fit in a GPU. If the number is set to -1, there is no separation and all GPUs are used for both generators and discriminators (only works for low-res images).
- n_frames_G: the number of input frames to feed into the generator network; i.e., n_frames_G - 1 is the number of frames we look into the past. the default is 3 (conditioned on previous two frames).
- n_frames_D: the number of frames to feed into the temporal discriminator. The default is 3.
- n_scales_spatial: the number of scales in the spatial domain. We train from the coarsest scale and all the way to the finest scale. The default is 3.
- n_scales_temporal: the number of scales for the temporal discriminator. The finest scale takes in the sequence in the original frame rate. The coarser scales subsample the frames by a factor of n_frames_D before feeding the frames into the discriminator. For example, if n_frames_D = 3 and n_scales_temporal = 3, the discriminator effectively sees 27 frames. The default is 3.
- max_frames_per_gpu: the number of frames in one GPU during training. If your GPU memory can fit more frames, try to make this number bigger. The default is 1.
- max_frames_backpropagate: the number of frames that loss backpropagates to previous frames. For example, if this number is 4, the loss on frame n will backpropagate to frame n-3. Increasing this number will slightly improve the performance, but also cause training to be less stable. The default is 1.
- n_frames_total: the total number of frames in a sequence we want to train with. We gradually increase this number during training.
- niter_step: for how many epochs do we double n_frames_total. The default is 5.
- niter_fix_global: if this number if not 0, only train the finest spatial scale for this number of epochs before starting to fine-tune all scales.
- batchSize: the number of sequences to train at a time. We normally set batchSize to 1 since often, one sequence is enough to occupy all GPUs. If you want to do batchSize > 1, currently only batchSize == n_gpus_gen is supported.
- no_first_img: if not specified, the model will assume the first frame is given and synthesize the successive frames. If specified, the model will also try to synthesize the first frame instead.
- fg: if specified, use the foreground-background separation model.
For other flags, please see options/train_options.py and options/base_options.py for all the training flags; see options/test_options.py and options/base_options.py for all the test flags.

Citation

If you find this useful for your research, please cite the following paper.

@article{wang2018vid2vid,
  title={Video-to-Video Synthesis},
  author={Ting-Chun Wang and Ming-Yu Liu and Jun-Yan Zhu and Guilin Liu and Andrew Tao and Jan Kautz and Bryan Catanzaro},  
  journal={arXiv preprint arXiv:1808.06601},
  year={2018}
}

Acknowledgments

We thank Karan Sapra, Fitsum Reda, and Matthieu Le for generating the segmentation maps for us. We also thank Lisa Rhee for allowing us to use her dance videos for training. We thank William S. Peebles for proofreading the paper.
This code borrows heavily from pytorch-CycleGAN-and-pix2pix and pix2pixHD.

lenoregilbert/vid2vid