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1. Stand still, take a photo from your front side, take a photo from your back side.
2. Prepare you favourite dance video, split it to frames, and save into the images folder

Then you can get the motion_video.mp4, you are dancing~

• input

front and back image

favourite dance video

• output

This project is built on the great and useful projects: textured_smplx, romp, smplify-x, humannerf

Since the complex dependence, basically you can refer to textured_smplx and romp

choose one way to run the code:

1. run pipeline directly
2. run step by step

python pipeline.py data/obj1 data/obj1/images/P01125-150055.jpg data/obj1/images/P01125-150146.jpg 

# prepare a folder of frames in images/, if you have the video, try ffmpeg or use romp deal with video directly.
romp --mode=video --calc_smpl --render_mesh -i=images/ -o=romp_output/ -t -sc=1. 

Then get npz with SMPL params sequences.

example can be find in ./data/obj1/images

openpose.bin --display 0 --render_pose 1 --image_dir ./data/obj1/images --write_json ./data/obj1/keypoints --write_images ./data/obj1/pose_images --hand --face

Please follow the instruction here

python smplifyx/main.py --config cfg_files/fit_smpl.yaml --data_folder ../data/obj1 --output_folder ../data/obj1/smpl  --model_folder models --vposer_ckpt V02_05

data_folder should contain images folder and keypoints folder in ../data/obj1, and the output contain fitted obj and pkl (SMPL param relative)

run python demo.py data_path front_img back_img smplx

run python prepare_smpl_sequences to get images of novel pose save the images in motion_snapshots folder

run ffmpeg, for example:

ffmpeg -f image2 -i motion_snapshots/%06d.png motion_video.mp4