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Can someone explain what's going on in this example from the gallery? The prompt is "a humanoid robot using a rolling pin to roll out dough": https://dreamfusion-cdn.ajayj.com/gallery_sept28/crf20/a_DSL... But if you look closely, the pin looks like it's actually rolling across the dough as the camera orbits.
From a pile of random undifferentiated images the model has learned the detailed 3D structure and plausible poses and variants of thousands (millions?) of everyday objects. And all we needed to get that 3D information out of the model was the right sampling procedure.
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From the abstract: “We introduce a loss based on probability density distillation that enables the use of a 2D diffusion model as a prior for optimization of a parametric image generator. Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss.” This seems like b…
Time and time again these ML techniques are proving to be wildly modular and pluggable. Maybe sooner or later someone will build a framework for end to end text-to-effective-ML-architecture that will just plug different things together and optimize them.
They have others as well.
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From the abstract: “We introduce a loss based on probability density distillation that enables the use of a 2D diffusion model as a prior for optimization of a parametric image generator. Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss.” This seems like b…
> This seems like basically plugging a couple of techniques together that already existed as with a majority of ML research
Amazing! How long then until we get photorealistic AI generated 3D VR games and experiences in the metaverse?
Why the downvote? I wasn't being sarcastic, it was a honest question, I'm really impressed how far this technology has come since GPT-3 2 years ago to DALl-E and Stable Diffusion ro Meta's text to video to this...
It’s totally sci-fi, and at the same time seems to be possible? I am amazed how even image generation evolved over the last year, but that’s just me daydreaming.
The most incredible thing here is that this demonstrates a level of 3D understanding that I didn't believe existed in 2D image models yet. All of the 3D information in the output was inferred from the training set, which is exclusively uncurated and unsorted 2D still images. No 3D models, no camera parameters, no depth maps. No information about picture content other than a text label (scraped from the web and often…
"For he spoke, and it came to be;
he commanded, and it stood firm."
Psalm 33:9, NIV
:)
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They're AI generated, the singularity already happened but the machines are trying to ease us into it.
Scary fn thought! and I agree with you! And the OP comment its by the magnanimous/infamous AnigBrowl You need to start doing AI legal admin ( I dont have the terms, but you may - we need legal language to control how we deal with AI) and @dang - kill the gosh darn "posting too fast" thing Jiminey Crickets I have talked to you abt this so many times...
I'm a kotlin programmer so it's a scary fun thought for me.
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Why the downvote? I wasn't being sarcastic, it was a honest question, I'm really impressed how far this technology has come since GPT-3 2 years ago to DALl-E and Stable Diffusion ro Meta's text to video to this...
I was wondering the same thing in the other thread about Text to Video. Someone asked about 3D Blender models, which made me think about animating blender models. Bang, now on this thread we see animated images… it does feel like we can get to asking for a 3D environment, put on a VR glass and experience it. And with outpainting, that we can even change it in real time. It’s totally sci-fi, and at the same time seems…