DeepFaceLive VS ncnn

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...

ncnn uses Vulkan for GPU acceleration, I've seen it used in a few projects to get AMD hardware support.

https://github.com/Tencent/ncnn

They're using NCNN to package the model. Have a look. https://github.com/Tencent/NCNN

Hi. Here is my prefered android app for realtime objet detection: https://github.com/nihui/ncnn-android-nanodet ; https://github.com/Tencent/ncnn contains a lot of android demo app for a lot of models.

ncnn home page: https://github.com/Tencent/ncnn

ncnn

Yes, Android devices tend to have bigger RAMs, making running 1024x1024 possible (this is not possible at all on iPhones, which could peak around 5GiB memory with my current implementation, some serious engineering required to bring that down on iPhone devices). The problem is I am not sure about speed. I would likely switch to NCNN (https://github.com/Tencent/ncnn) as the backend which have a decent Vulkan computing kernel support. It is definitely a possibility and there is a path to do that.

As the leading open-source inference framework in China and in the world, what we like are its almost zero cost cross-platform capability, high inference speed, and minimal deployment volume. (Project address: https://github.com/Tencent/ncnn)

If you’re not planning on training the neural net on an embedded device and just do inference, this might interest you: https://github.com/Tencent/ncnn