Show HN: Blotter – An interactive, never ending music video (twitch.tv)
One day I was listening to a playlist and wished there could be some cool visuals to go along with it.
Blotter is a proof of concept I hacked together that does a bit of audio recognition combined with a few generative AI models (both text and img) to create visuals that are relevant to the song.
The video stream is generated in real time at 24fps - you can try it yourself by requesting visuals in the Twitch chat using the "!v" command!
Right now it's mostly a fun hack project, but I am tinkering with new model architectures for higher fidelity video as well as an interactive tool so people can make videos with their own audio files.
I'd love to hear any feedback or suggestions, thanks!
40 comments
[ 0.27 ms ] story [ 93.6 ms ] threadSlippery slope I say this.
Or humans need to chill out, realize we're a bunch of primates on a space rock, and stop being so offended and afraid.
So what if we use politicians, world leaders, and celebs? This is no different than photoshop.
You know what's funny?
https://www.youtube.com/watch?v=IkaAZE_UGMo
https://www.youtube.com/shorts/ivhUH68KWis
Or something along those lines:
https://www.youtube.com/watch?v=03vmbKGW1iI
This is even funnier to me because I have no idea what any of this means. Kids love this stuff, though, and they're wildly creative.
We're just getting started with the creative Cambrian explosion. We have yet to hit the inflection point.
Gonna add cleaning things up a bit and writing some documentation to my to-do list
Always loved things that go in time with music
Thanks for taking the time to check it out either way!
is there a way to apply a little stabilization to consecutive outputs, something like https://www.youtube.com/watch?v=X1WG5jc5SU0 ?
This feels like a solid glimpse of the truly new things that we'll likely see take shape soon.
Hoping to clean it up a bit and share an e2e repo soon
Thanks for checking out the stream!
Thanks for checking out the stream!
That being said, one of my biggest takeaways from this whole experience was I could've just trained a Shrek generator and it would've covered like 90% of use cases