/DVD-GAN

A pytorch implementation of Efficient Video Generation on Complex Datasets

Primary LanguagePython

DVD-GAN

This repo is an implementation of Efficient Video Generation on Complex Datasets

Prerequisite

Package version
python >=3.5
pytorch 1.12
numpy 1.17.2
pandas 0.25.1
tensorboardX 1.8
ffmpeg 3.4.2

Note: For more detail, please look up requirements.txt

Prepare datasets

sudo apt install ffmpeg # important package
chmod u+x scripts/data_prepare.sh
scripts/data_prepare.sh <dataset_path>

Train the model

scripts/train_model.sh <runing_mode> <dataset_path>

Dataset

Process UCF-101

  • Step 1: Download dataset
  • Step 2: Convert from avi to jpg files using: python utils/video_jpg_ucf101_hmdb51.py avi_video_directory jpg_video_directory
  • Step 3: Generate n_frames files using: python utils/n_frames_ucf101_hmdb51.py jpg_video_directory
  • Step 4: Generate annotation file in json format similar to ActivityNet using: python utils/ucf101_json.py annotation_dir_path

Note: To change the number of class:

  • Modify classInd.txt to contain the expected class(es). For example: 1 ApplyEyeMakeup 2 ApplyLipstick 3 Archery
  • Run step 4 only
  • The code in dataloader automatically skips the unsed videos.