Comments (1)
The dataset format is the same as FOMM. You can refer to FOMM's README to build your own dataset.
Training on your own dataset
- Resize all the videos to the same size e.g 256x256, the videos can be in '.gif', '.mp4' or folder with images. We recommend the later, for each video make a separate folder with all the frames in '.png' format. This format is loss-less, and it has better i/o performance.
- Create a folder data/dataset_name with 2 subfolders train and test, put training videos in the train and testing in the test.
- Create a config config/dataset_name.yaml, in dataset_params specify the root dir the root_dir: data/dataset_name. Also adjust the number of epoch in train_params.
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Related Issues (20)
- Full frame HD talking head with driving audio
- getting this error when trying to run the demo.py on Google Colab HOT 1
- NotImplementedError: A UTF-8 locale is required. Got ANSI_X3.4-1968 HOT 1
- The code of evaluate model using L1 (AKD, MKR) AED
- how to make inference on ted dataset HOT 3
- strange issue on replicate HOT 1
- Equivariance loss
- Bad result about eyeglasses HOT 1
- ImportError: No module named matplotlib
- Different Aspect Ratios HOT 1
- T3d oon faces?
- limitation on time
- how to process download Voxceleb2 data HOT 1
- Black contribution masks
- Discriminator module
- head cutn the upper side.
- Avd network confusion
- face-alignment changed code HOT 1
- More driving videos
- Get Better Quality
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from thin-plate-spline-motion-model.