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Deep-motion-editing

This library provides fundamental and advanced functions to work with 3D character animation in deep learning with Pytorch. The code contains end-to-end modules, from reading and editing animation files to visualizing and rendering (using Blender) them.

Prerequisites

  • Linux or macOS
  • Python 3
  • CPU or NVIDIA GPU + CUDA CuDNN

Quick Start

We provide pretrained models together with demo examples using animation files specified in bvh format.

Motion Retargeting

The retargeted demo results, that consists both intra-structual retargeting and cross-structural retargeting, will be saved in retargeting/pretrained/results.

Motion Retargeting

Dataset

We use Mixamo dataset to train our model. You can download our preprocessed data from Google Drive or Baidu Disk(4rgv). Then place the Mixamo directory within retargeting/datasets.

Otherwise, if you want to download Mixamo dataset or use your own dataset, please follow the instructions below. Unless specifically mentioned, all script should be run in retargeting directory.

  • To download Mixamo on your own, you can refer to this good tutorial. You will need to download as fbx file (skin is not required) and make a subdirectory for each character in retargeting/datasets/Mixamo. In our original implementation we download 60fps fbx files and downsample them into 30fps. Since we use an unpaired way in training, it is recommended to divide all motions into two equal size sets for each group and equal size sets for each character in each group. If you use your own data, you need to make sure that your dataset consists of bvh files with same t-pose. You should also put your dataset in subdirectories of retargeting/datasets/Mixamo.

  • Enter retargeting/datasets directory and run blender -b -P fbx2bvh.py to convert fbx files to bvh files. If you already have bvh file as dataset, please skip this step.

  • In our original implementation, we manually split three joints for skeletons in group A. If you want to follow our routine, run python datasets/split_joint.py. This step is optional.

  • Run python datasets/preprocess.py to simplify the skeleton by removing some less interesting joints, e.g. fingers and convert bvh files into npy files. If you use your own data, you'll need to define simplified structure in retargeting/datasets/bvh_parser.py. This information currently is hard-coded in the code. See the comment in source file for more details. There are four steps to make your own dataset work.

  • Training and testing character are hard-coded in retargeting/datasets/__init__.py. You'll need to modify it if you want to use your own dataset.

Train

After preparing dataset, simply run

cd retargeting
python train.py --save_dir=./training/

It will use default hyper-parameters to train the model and save trained model in retargeting/training directory. More options are available in retargeting/option_parser.py. You can use tensorboard to monitor the training progress by running

tensorboard --logdir=./retargeting/training/logs/