The VVT dataset only features upper clothing for tryon. You may want collect your own data for more expansive tryon.
The VVTDataset
expects the directory structure to look like this:
DATASET_ROOT/
- clothes_person
- img (images of the target user and garment)
- PERSON_IDS...
- keypoint (we do not use these)
- PERSON_IDS...
- parsing (we do not use these)
- PERSON_IDS...
- train
- cloth (warped cloths, generated by WarpModule)
- PERSON_IDs...
- densepose (generated by detectron2)
- PERSON_IDs...
- optical_flow (generated by flownet2)
- PERSON_IDs...
- train_frames (original video frames)
- PERSON_IDs...
- train_frames_keypoint (cocopose annotations, optional if using densepose)
- PERSON_IDs...
- train_frames_parsing (LIP annotations)
- PERSON_IDs...
- test
- cloth (warped cloths, generated by WarpModule)
- PERSON_IDs...
- densepose (generated by detectron2)
- PERSON_IDs...
- optical_flow (generated by flownet2)
- PERSON_IDs...
- train_frames (original video frames)
- PERSON_IDs...
- train_frames_keypoint (cocopose annotations, optional if using densepose)
- PERSON_IDs...
- train_frames_parsing (LIP annotations)
- PERSON_IDs...
Each folder should contain subfolders for the video of each person (PERSON_ID). Each subfolder contains the files that represent the data of each video frame.
The data under corresponding PERSON_IDs should match 1-to-1.
For example: cloth/ABC/
should have the same number of files as densepose/ABC/
.
If you want to define your own folder layout, you can extend our TryonDataset
class
in datasets/tryon_datasets.py
and override the @abstractmethod
s that fetch each
input path.
See VVTDataset
as a reference.