11–20 of 27 posts
If the massively online education people had that kind of quality, maybe people would actually finish the courses.
I find this ... disquieting. I think its fantastic but I also find something about the lack of uncanny valley troubling. I should feel happier about it, but I can't stop feeling a bit odd that a sketch can go to photorealistic north of the bad so well now: I expected 5-10 more years for this.
I'm not sure that I agree, the only images on the linked page are pretty tiny thumbnails, and the poorly compressed video - where I can definitely see some artifacting already that doesn't seem to be caused by the video compression, there's no way to know if they actually did a good job from that.
If this was intruded background in a movie at HQ, I'm not sure I could always tell the difference although its equally possible the renders would be unsustainable under motion, not enough real-world change to look "real" and it re-enters uncanny valley
Imagine the impact on moviemaking that this will have within just a few iterations of processing power. Thousands of hours of visual effect artist work in film and TV will soon be abstracted into some high-level commands, transformed by software into moving film. Very exciting.
Imagine the impact on moviemaking that this will have within just a few iterations of processing power. Thousands of hours of visual effect artist work in film and TV will soon be abstracted into some high-level commands, transformed by software into moving film. Very exciting.
Hard to tell if this impact will be negative or not.
NVIDIA just released the code: https://github.com/nvlabs/spade/
If you want to see the same thing done 18 years ago without new-age machine learning, read https://www.mrl.nyu.edu/projects/image-analogies/index.html IMO the most elegant vision/graphics algorithm ever written. Specifically this is the "texture-by-numbers" application. Ex: https://www.mrl.nyu.edu/projects/image-analogies/potomac.htm... Every single fancypants application of neural nets in graphics today is a retread…
NVIDIA just released the code: https://github.com/nvlabs/spade/
What software do you use to make those 3D DNN architecture images?
NVIDIA just released the code: https://github.com/nvlabs/spade/
NVIDIA just released the code: https://github.com/nvlabs/spade/
What software do you use to make those 3D DNN architecture images?
NVIDIA just released the code: https://github.com/nvlabs/spade/
"To reproduce the results reported in the paper, you would need an NVIDIA DGX1 machine with 8 V100 GPUs."
That said, I'm not sure why one wouldn't get a similar result training on the EC2 or GCE instances that have 8 V100s. Or even training with fewer GPUs but accumulating gradients to get the same batch size.