Hey guys!

Looking for papers on Speech-driven Face Animation (or) Visual dubbing where given an audio + a video containing a face –> Output is a video of the face lip-synced to the audio.

Garrdio et al “VDub: Modifying Face Video of Actors for Plausible Visual Alignment to a Dubbed Audio Track” does this, but they don’t seem to have released their code.

Was wondering if there are any newer implementations.

Thanks!

A paper published yesterday in Nature’s “Matters Arising” shows that logistic regression with just two parameters can achieve the same performance as the deep learning approach published in Nature last August, which was previously discussed in this subreddit here and here.

I’m playing with super resolution networks (SRResNet, SRGAN, EDSR, etc.) and I don’t really understand how these things can function as generative process given that you’re not feeding noise into the generator.

Just saw this on the project’s official website:

Q: How are you protecting against adversaries who will try to access the code and data? A: We will be gating access to the training dataset so only researchers accepted into the challenge can access it. Each participant will need to agree to terms of use on how they use, store, and handle the data, and there are strict restrictions on who else the data can be shared with.

Any idea who they’re counting as a “researcher”?

I’m guessing they’re limiting this to people tied to accredited labs or institutions, and hobbyists are out of luck.

As an exercise for myself I decided to implement SWA, from the paper Averaging Weights Leads to Wider Optima and Better Generalization. I did it with Keras and decided it might make a nice package.

Repo:

https://github.com/simon-larsson/keras-swa

pip:

pip install keras-swa

If you are not familiar with SWA, it is a trick to approximate ensembling by taking a running average of your weights towards the end of training a model. You can read more in this nice blog post explaining SWA and it’s relatives SSE and FGE.

I currently only implement the constant learning rate schedule from the paper, hoping to add the cyclic one from the paper soon. It is also possible to leave the learning rate to the optimizer or other schedulers. I have also not implemented the batch normalization fix. It requires a forward pass over training data, which I don’t know how to do from a callback. So any help there would be appreciated.

I would love for people to try it! Feedback is also welcome! 🙂

Hey guys!

Looking for papers on Face Reenactment. I’ve gone through Face2Face and the results look pretty good! Was wondering if there are any newer implementations.