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README.md

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+# Layered Neural Rendering in PyTorch
+
+This repository contains training code for the examples in the SIGGRAPH Asia 2020 paper "[Layered Neural Rendering for Retiming People in Video](https://retiming.github.io/)."
+
+This is not an officially supported Google product.
+
+
+## Prerequisites
+- Linux
+- Python 3.6+
+- NVIDIA GPU + CUDA CuDNN
+
+## Installation
+This code has been tested with PyTorch 1.4 and Python 3.8.
+
+- Install [PyTorch](http://pytorch.org) 1.4 and other dependencies.
+  - For pip users, please type the command `pip install -r requirements.txt`.
+  - For Conda users, you can create a new Conda environment using `conda env create -f environment.yml`.
+
+## Data Processing
+- Download the data for a video used in our paper (e.g. "reflection"):
+```bash
+bash ./datasets/download_data.sh reflection
+```
+- Or alternatively, download all the data by specifying `all`.
+- Download the pretrained keypoint-to-UV model weights:
+```bash
+bash ./scripts/download_kp2uv_model.sh
+``` 
+The pretrained model will be saved at `./checkpoints/kp2uv/latest_net_Kp2uv.pth`.
+- Generate the UV maps from the keypoints:
+```bash
+bash datasets/prepare_iuv.sh ./datasets/reflection
+```
+## Training
+- To train a model on a video (e.g. "reflection"), run:
+```bash
+python train.py --name reflection --dataroot ./datasets/reflection --gpu_ids 0,1
+```
+- To view training results and loss plots, visit the URL http://localhost:8097.
+Intermediate results are also at `./checkpoints/reflection/web/index.html`.
+
+You can find more scripts in the `scripts` directory, e.g. `run_${VIDEO}.sh` which combines data processing, training, and saving layer results for a video. 
+
+**Note**:
+- It is recommended to use >=2 GPUs, each with >=16GB memory.
+- The training script first trains the low-resolution model for `--num_epochs` at `--batch_size`, and then trains the upsampling module for `--num_epochs_upsample` at `--batch_size_upsample`.
+If you do not need the upsampled result, pass `--num_epochs_upsample 0`.
+- Training the upsampling module requires ~2.5x memory as the low-resolution model, so set `batch_size_upsample` accordingly.
+The provided scripts set the batch sizes appropriately for 2 GPUs with 16GB memory.
+- GPU memory scales linearly with the number of layers.
+
+## Saving layer results from a trained model
+- Run the trained model:
+```bash
+python test.py --name reflection --dataroot ./datasets/reflection --do_upsampling
+```
+- The results (RGBA layers, videos) will be saved to `./results/reflection/test_latest/`.
+- Passing `--do_upsampling` uses the results of the upsampling module. If the upsampling module hasn't been trained (`num_epochs_upsample=0`), then remove this flag.
+
+## Custom video
+To train on your own video, you will have to preprocess the data:
+1. Extract the frames, e.g.
+    ```
+    mkdir ./datasets/my_video && cd ./datasets/my_video 
+    mkdir rgb && ffmpeg -i video.mp4 rgb/%04d.png
+    ```
+1. Resize the video to 256x448 and save the frames in `my_video/rgb_256`, and resize the video to 512x896 and save in `my_video/rgb_512`.
+1. Run [AlphaPose and Pose Tracking](https://github.com/MVIG-SJTU/AlphaPose) on the frames. Save results as `my_video/keypoints.json`
+1. Create `my_video/metadata.json` following [these instructions](docs/data.md).
+1. If your video has camera motion, either (1) stabilize the video, or (2) maintain the camera motion by computing homographies and saving as `my_video/homographies.txt`.
+See `scripts/run_cartwheel.sh` for a training example with camera motion, and see `./datasets/cartwheel/homographies.txt` for formatting.
+
+**Note**: Videos that are suitable for our method have the following attributes:
+- Static camera or limited camera motion that can be represented with a homography.
+- Limited number of people, due to GPU memory limitations. We tested up to 7 people and 7 layers.
+Multiple people can be grouped onto the same layer, though they cannot be individually retimed.
+- People that move relative to the background (static people will be absorbed into the background layer).
+- We tested a video length of up to 200 frames (~7 seconds).
+
+## Citation
+If you use this code for your research, please cite the following paper:
+```
+@inproceedings{lu2020,
+  title={Layered Neural Rendering for Retiming People in Video},
+  author={Lu, Erika and Cole, Forrester and Dekel, Tali and Xie, Weidi and Zisserman, Andrew and Salesin, David and Freeman, William T and Rubinstein, Michael},
+  booktitle={SIGGRAPH Asia},
+  year={2020}
+}
+```
+
+## Acknowledgments
+This code is based on [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).