Currently the huggingface model is only for direct feature extraction without any video pre-processing (e.g. face detection, cropping, strided window, etc).

python

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained(
    "ControlNet/marlin_vit_large_ytf",  # or other variants
    trust_remote_code=True
)
tensor = torch.rand([1, 3, 16, 224, 224])  # (B, C, T, H, W)
output = model(tensor)  # torch.Size([1, 1568, 384])

License

This project is under the CC BY-NC 4.0 license. See LICENSE for details.

References

If you find this work useful for your research, please consider citing it.

bibtex

@inproceedings{cai2022marlin,
  title = {MARLIN: Masked Autoencoder for facial video Representation LearnINg},
  author = {Cai, Zhixi and Ghosh, Shreya and Stefanov, Kalin and Dhall, Abhinav and Cai, Jianfei and Rezatofighi, Hamid and Haffari, Reza and Hayat, Munawar},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  month = {June},
  pages = {1493-1504},
  doi = {10.1109/CVPR52729.2023.00150},
  publisher = {IEEE},
}

ControlNet/marlin_vit_large_ytf

MARLIN: Masked Autoencoder for facial video Representation LearnINg

Use transformers (HuggingFace) for Feature Extraction

License

References

ControlNet/
marlin_vit_large_ytf

Use `transformers` (HuggingFace) for Feature Extraction