Dance Dance Generation

[Paper]

Pytorch implementation of Dance Dance Generation: Motion Transfer for Internet Videos.

Acknowledgments

This code is heavily built on the top of Pix2pixHD. We thank the authors for their great work as well as releasing the code.

Getting Started

Prerequisites

Python 2.x / Python 3.x
Pytorch 0.4.0+
Nvidia GPU (12G+ memory recommended)
According CUDA + CuDNN

Training

The two-stage motion transfer model has been trained under two resolutions: 256x256 (low res) and 512x512 (high res). Please note that the number of filters of the first conv layer (--ngf) is half size of the original pix2pixHD work for the memory limitation. Feel free to increase the number to further improve the generation quality if GPU memory allowed (haven't been fully tested).

Train low res model:

python train.py --name personal_transfer_256p --resize_or_crop none --dataroot PATH_TO_TRAIN_DATA --label_nc 75 --comb_label_nc 48 --no_instance --ngf 32 --batchSize 4 --lambda_sp 10.0

Train high res model:

python train.py --name personal_transfer_512p --resize_or_crop none --dataroot PATH_TO_TRAIN_DATA --label_nc 75 --comb_label_nc 48 --no_instance --netG local --ngf 16 --num_D 3 --niter 50 --niter_decay 50 --niter_fix_global 10 --load_pretrain PATH_TO_LOW_RES_MODEL --batchSize 1 --lambda_sp 10.0

For data parallelism using multiple GPUs, simply make (e.g. using 4 GPUS)

--batchSize 4 --gpu_ids 0,1,2,3

Testing

Test low res model:

python test.py --name personal_transfer_256p --resize_or_crop none --dataroot PATH_TO_TEST_DATA --label_nc 75 --comb_label_nc 48 --no_instance --ngf 32

Test high res model:

python test.py --name personal_transfer_512p --resize_or_crop none --dataroot PATH_TO_TEST_DATA --label_nc 75 --comb_label_nc 48 --no_instance  --netG local --ngf 16

Yipin/Dance_Dance_Generation