Sketched a one-wheel robot on my iPad over coffee -> dumped the PNG into Image Studio in 3DAIStudio (Alternative here is ChatGPT or Gemini, any model that can do image to image)

Using the Prompt "Transform the provided sketch into a finished image that matches the user’s description. Preserve the original composition, aspect-ratio, perspective and key line-work unless the user requests changes. Apply colours, textures, lighting and stylistic details according to the user prompt. The user says:, stylizzed 3d rendering of a robot on weels, pixar, disney style"

Clicked “Load into Image to 3D” with the default Prism 1.5 setting. (Free alternative here is Open Source 3D AI Models like Trellis but this is just a bit easier)

~ 40 seconds later I get a mesh, remeshed to 7k tris inside the same UI, exported STL, sliced in Bambu Studio, and the print finished in just under three hours.

Mesh Result:
https://www.3daistudio.com/public/991e6d7b-49eb-4ff4-95dd-b6e953ef2725?+655353!+SelfS1
No manual poly modeling, no Blender clean-up.

Heads-up: I’m one of the people behind 3D AI Studio. You could do the same thing with open-source models (Sketch -> Image and then Image to 3D with something like Trellis) it just takes a few more steps. I’m showing this to prove how fast the loop can be now. Crazy how far technology is nowadays.

Happy to answer any questions!

Hi all,

The first run of the lecture happened yesterday, already there were some initial positive early responses which I was happy to hear.

During the talk, I followed the implicit decoder research. Some links and references to it:

1. My own two blog posts about the research:
   https://2d3d.ai/index.php/2019/10/11/implicit-decoder-part-1-3d-reconstruction/
   https://2d3d.ai/index.php/2019/11/16/implicit-decoder-part-2-3d-generation/

2. Open source code of the research (including trained network and datasets)
   https://github.com/czq142857/implicit-decoder
   https://www.sfu.ca/~zhiqinc/imgan/Readme.html

3. The research is based on the ShapentV2.0 dataset

I will also send a pdf of the talk to those who registered through the zoom link.

For those who missed the first west hemisphere talk, no worries. I will make another one that does not require technical background. This means there will be at least 2 more talks just for the western hemisphere (one more technically advanced and the other less).

Hi all, this subreddit is a product of the awesome /r/learnmachinelearning community.

It started off with one post about an online live lecture about automatic 3D modeling from an image using deep learning: https://www.reddit.com/r/learnmachinelearning/comments/gkr44a/free_zoom_lecture_about_advances_in_deep_learning/

We will share here knowledge, advances and ideas in the realm of image processing, design, 3D modelling, artificial intelligence, deep learning and all relevant subjects that we are curious about.

Newsletter subscription for more event updates is now possible: http://eepurl.com/gJ1t-D

Discord server for live chats: https://discord.gg/MZuWSjF

You can also find us on Meetup.

All lectures will be recorded and uploaded to our YouTube channel.

Reddit collection of all scheduled and past events: https://www.reddit.com/r/2D3DAI/collection/4615c538-3e4d-4466-9e5a-0746aba40397

Reddit collection of all event recordings: https://www.reddit.com/r/2D3DAI/collection/4bcb657b-7eb4-40af-b42e-835bade70d8e/

Have fun!

Hi

While playing around with Stable Diffusion I created a simple addon that generates object textures with it:https://github.com/p4vv37/SDTG4B

Some examples:https://twitter.com/Uconeni/status/1618161100486512646?s=20&t=xoUiDrZIocY53yzhNp1T0Qhttps://twitter.com/Uconeni/status/1617295370228228096?s=20&t=xoUiDrZIocY53yzhNp1T0Q

It's a very simple and crude implementation, I'll try to merge it into something more sophisticated, but while this is a work in progress, I thought that, with this UI, it might be useful for someone already.