Image technology was something we could only imagine, but it eventually became a reality with the help of Dall-E & Mid journey and Stable Diffusion. An updated Stable Diffusion solution for creating smooth textures is available in Blender, an open-source 3D program.

We were all aware that text-to-3D technology will soon be available. AI is undoubtedly affecting how 3D apps are created today and in the future.

Check out DreamFusion

The text-to-image method needs huge datasets of annotated 3D assets and effective designs for 3D data denoising, neither of which are currently available. In this work, DreamFusion circumvents these constraints by performing text-to-3D synthesis utilizing a pre-trained 2D text-to-image diffusion model.

All of the models are not currently downloadable. All of these examples are intended to stretch the capabilities of AI. I believe that this is the best 3D model produced by AI yet. The AI creates geometries that are molded. There are many lovely items in their gallery, which you can view. It’s fascinating to observe the outcome and how the AI processes the text to produce the 3D model.

AI is developing quickly—in my opinion, far too quickly. Keeping up is becoming challenging.

Dall-E2 was just six months ago released by OpenAI.

Stability AI’s Stable Diffusion was released less than two months ago.

Meta recently unveiled a new AI tool that converts text into video.

Warning: 3D artists.

Your world may be turned upside down by this technology, both positively and negatively. Today, 3D models are still created by hand using modeling programs like Blender or ZBrush, which takes hours of work from the human artist.

soon be able to create stunning 3D artwork with only a short text prompt.

But not all is gloom and doom for working artists. not yet, at least.

The assets themselves are currently unsuitable for commercial use and have a low resolution.

What It Does

A pre-trained 2D image text-diffusion model is converted into a 3D object synthesis model via DreamFusion.

A 3D scene is optimized using the “Imagen” text-to-image diffusion model. The Google team also suggests using the Score Distillation Sampling (SDS) approach to optimize samples in a 3D environment with any number of parameters.