DeepFaceLive VS Wav2Lip

https://github.com/iperov/DeepFaceLive. This is the first result Google gives me. Rudimentary, but enough to possibly trick someone who isn't knowledgeable enough. Instead of a fully AI generated person, it is a digital face swap. Essentially, you are correct in that the technological leap towards making a fully interactive deep fake is likely far beyond current technological capabilities. Especially for a scammer. Digitally swapping a face sidesteps that technological limitation.

A more lifelike deformer would be running a 'deepfake' layer over your face motion into your 2D character face, but so far I haven't tried it yet. Here is some example of a well known open source 'faceswapper' : https://github.com/iperov/DeepFaceLive

DeepFaceLive https://github.com/iperov/DeepFaceLive You have to create a celeb model first or download a publicly available model

"Advanced AI" is an expression that makes politicians and decision makers to go on a rampage. Don't get your hopes up, the way the world reacted to ChatGPT, Stable Diffusion and stuff like that already suggests all AIs will be pretty much government regulated eventually and people like Eugenia has no way to circumvent this. Add projects like https://github.com/iperov/DeepFaceLive into the mix and you can see the perfect storm brewing up already...

Hey HN, we’re sync. (https://synclabs.so/). We’re building fast + lightweight audio-visual models to create, modify, and understand humans in video.

You can check our more about us and our company in this video here: https://bit.ly/3TV27rd

Our first api lets you lip-sync a person in a video to an audio in any language in zero-shot. You can check out some examples here (https://bit.ly/3IT3UXk)

Here’s a demo showing how it works and how to sync your first video / audio: https://bit.ly/4ablRwo

Our playground + api is live, you can play with our models here: https://app.synclabs.so/

Four years ago we open-sourced Wav2lip (https://github.com/Rudrabha/Wav2Lip), the first model to lipsync anyone to any audio w/o having to train for each speaker. Even now, it’s the most prolific lipsyncing model to date (almost 9k GitHub stars).

Human lip-sync enables interesting features for many products – you can use it to seamlessly translate videos from one language to another, create personalized ads / video messages to send to your customers, or clone yourself so you never have to record a piece of content again.

We’re excited about this area of research / the models we’re building because they can be impactful in many ways:

[1] we can dissolve language as a barrier

check out how we used it to dub the entire 2-hour Tucker Carlson interview with Putin speaking fluent English: https://vimeo.com/914605299

imagine millions gaining access to knowledge, entertainment, and connection — regardless of their native tongue.

realtime at the edge takes us further — live multilingual broadcasts + video calls, even walking around Tokyo w/ a Vision Pro 2 speaking English while everyone else Japanese.

[2] we can move the human-computer interface beyond text-based-chat

keyboard / mice are lossy + low bandwidth. human communication is rich and goes beyond just the words we say. what if we could compute w/ a face-to-face interaction?

Many people get carried away w/ the fact LLMs can generate, but forget they can also read. The same is true for these audio/visual models — generation unlocks a portion of the use-cases, but understanding humans from video unlocks huge potential.

Embedding context around expressions + body language in inputs / outputs would help us interact w/ computers in a more human way.

[3] and more

powerful models small enough to run at the edge could unlock a lot:

eg.

If your technically inclined you can use https://github.com/Rudrabha/Wav2Lip to sync the lip movements to the new audio.

This is the Github link : https://github.com/Rudrabha/Wav2Lip

Video mapping onto Audio: Now you have Audio with coherent back and forth dialogue. To get the looped video puppets, you find a relatively stable interview clip (in this channel and many of Athenes other ones, the clips of the people just stay in one place). Then feed the audio + video clip into a lipsync algorithm like this https://bhaasha.iiit.ac.in/lipsync/

It's not animating really, just lip sync and face restoration, here I used: https://github.com/Rudrabha/Wav2Lip and https://github.com/TencentARC/GFPGAN respectively.

I just used the web based wav2lip demo. https://bhaasha.iiit.ac.in/lipsync/ Haven’t used the plan in a while, however the colab gives much better results. This was just a quick dusty example done all in the phone.

https://github.com/Rudrabha/Wav2Lip Demo: https://youtu.be/0fXaDCZNOJc