stable-dreamfusion VS zero123

Who is working on it? I've seen a few other models that do this, like Stable-Dreamfusion.

I haven't tried Stable-DreamFusion, but it might be able to take an input image along with a prompt?

similar to Magic3D and Dreamfusion / Stable-Dreamfusion, but this one looks a lot more vivid and detailed!

I've seen a few "text-to-3D" models that use Stable Diffusion. Zero-1-to-3 and Stable-DreamFusion appear to be capable of generating 3D models from text prompts.

Ever heard of Stable Dreamfusion ? Open source text to 3D mesh model

I followed the steps in this repo. They added my turtle as an example to the repo after their latest improvements. https://github.com/ashawkey/stable-dreamfusion

There is an (independent?) implementation here, released last week to 0.1, but it already has a 100 issues.

But there's stable-dreamfusion, still not good but you could try it.

Dreamfusion -- text-to-3d seems like it could be useful here: https://dreamfusion3d.github.io/ (once successfully opensourced, see https://github.com/ashawkey/stable-dreamfusion)

Rigging also looks like it could have a decent AI/DNN solution: https://arxiv.org/pdf/2005.00559.pdf

Someone with resources will have to train Zero123 [1] with this backbone.

[1] https://zero123.cs.columbia.edu/

This looks a fine-tune of the classic zero123 (https://github.com/cvlab-columbia/zero123) I’m excited to check out the quality improvements.

Though 3d model synthesis is one use case, I found the less advertised base reprojection model to be more useful for gamedev at the moment. You can generate a multiview spritesheet from an image, and it’s fast enough for synthesis during a gameplay session. I couldn’t get a good quality/time balance to do the same with the 3d models, and the lack of mesh rigging or animation combined with imperfections in a fully 3d model tends to break the suspension of disbelief compared to what players are used to. I’m this will change as the tech develops and we layer more AI on top (automatic animation synthesis is an active research area).

If you’re interested in this you might also want to check out deforum (https://github.com/deforum-art/deforum-stable-diffusion) which provides even more powerful camera controls on top of stable diffusion designed for full scenes rather than single objects.

As described in the stable-dreamfusion repo for the image to 3D using the zero123 model (you can read more about that in their repo here: https://github.com/cvlab-columbia/zero123) I used the 105000 checkpoint of zero123. It took about an hour to go through their initial NeRF generation and cleanup steps to get the model output.

Until then https://github.com/cvlab-columbia/zero123 was kinda okay, but practical results often left to be desired, from the imprecision of the view angles to the at times fanciful re-imaginations of the source object.

For anyone else who tried to download the weights and got Google Drive throwing a quota error at you, they're working on it: https://github.com/cvlab-columbia/zero123/issues/2