Text-to-3D
Turn a sentence into a 3D model
WHAT THIS CAPABILITY MEANS
Takes a text description and outputs a 3D asset: a mesh, a textured model or a renderable 3D representation. The output is neither an image nor a video but geometry that can be viewed from any angle and placed in a scene. Unlike image-to-3D it has no reference image at all, and unlike text-to-image its output carries a genuine third dimension.
How it is done
One route uses 2D generative models as supervision: a 2D diffusion model scores renderings from many viewpoints and that score optimises a 3D representation such as a neural radiance field or a Gaussian splat — score distillation. Another route trains a generator directly on large 3D datasets and emits mesh and texture in one or several steps. Output meshes often need retopology and decimation before entering standard rendering or game pipelines.
Representative products
2Organizations involved
Typical uses
- Draft props and sets for games and film
- Quick 3D showcases for products
- Turning industrial concepts into physical form
- 3D illustration for education
How it is evaluated
- CLIP similarity
- Semantic agreement between rendered views and the prompt
- Chamfer distance
- Mean surface-point distance between generated and reference meshes
- Human rating
- Ratings for shape completeness and texture quality
Limits and hard parts
- Generated meshes often come out as fragmented triangles and need heavy retopology before use
- Backfaces and interiors are guessed, producing hollows and intersections when you orbit the model
- Material, lighting and geometry are not properly separated, so the look breaks when exported to another engine
Concepts behind it
Diffusion Models
Learn a thousand tiny denoising steps, and you can build an image from pure noise
Multimodal Generation
One model that learns to speak, to draw, to move — even to model the 3D world
Latent Diffusion & Conditional Control
Run diffusion not over pixels, but inside a compressed semantic space