REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer. ACM MM 2022.

Prerequisites

• Ubuntu
• Python 3
• NVIDIA GPU (>12GB memory) + CUDA10 cuDNN7
• PyTorch 1.0.0

Other Dependencies

FlowNet_v2 (directly ported from the original [flownet2] following the steps described in [vid2vid])

cd models/flownet2_pytorch
bash install.sh

Getting Started

It's a preview version of our source code. We will clean it up in the near future.

Notes

1. Main functions for training and testing can be found in "train_stage1.py", "train_stage2.py", "train_stage2.py", "test_all_stages.py";
2. Data preprocessings of all the stages can be found in "data" folder;
3. Model definitions of all the stages can be found in "models" folder;
4. Training and testing options can be found in "options" folder;
5. Training and testing scripts can be found in "scripts" folder;
6. Tool functions can be found in "util" folder.

Data Preparation

Download all the data packages from [google drive] or [baidu pan (extraction code:gle4], and uncompress them. You should create a directory named 'iPER' in the root (i.e., 'iPER') of this project, and then put 'train' and 'test' folders to 'iPER' you just created. The structure should look like this:
-xx/xx ---iPER
------train
------test

Testing stage 1

bash scripts/full/test_stage1.sh

Testing stage 123

bash scripts/full/test_stage123.sh

Acknowledgement

A large part of the code is borrowed from NVIDIA/vid2vid. Thanks for their wonderful works.

REMOT