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TEXT → 3D • CURATED • UPDATED FEB 5, 2026

DreamFusion

Text-to-3D via NeRF with score distillation

Generates high-quality 3D NeRF (Neural Radiance Field) representations from text prompts using score distillation sampling, a technique that leverages pre-trained 2D diffusion models for 3D generation. Produces detailed 3D scenes and objects with realistic lighting, materials, and geometry from natural language descriptions. Enables creation of view-consistent 3D content without requiring 3D training data, making it ideal for generating complex 3D scenes, objects, and environments for visualization, games, and virtual reality applications. Pioneering approach uses 2D diffusion models to guide 3D NeRF generation, enabling high-quality 3D creation from text.

Pricing Free
Platforms local
API No
Open Source Yes
Modalities Text → 3D
Best For Best for Research
Date Added 2026-02-05

Pioneering NeRF-based text-to-3D generation using score distillation, representing a significant advancement in 3D content creation from text without requiring 3D training datasets.

Clone the Stable DreamFusion repository from GitHub. Follow installation instructions and set up dependencies. Download model weights and configure environment. Enter detailed text prompts describing 3D scenes or objects. Generate NeRF representations using score distillation. Review 3D quality, lighting, and view consistency. Iterate by refining prompts. Export NeRFs for visualization or conversion. Free and open-source, suitable for research and development.

1 Use detailed prompts for 3D scene descriptions
2 Review view consistency across angles
3 Experiment with lighting and material descriptions
4 Use for research and development workflows
5 Join community for support and examples
GitHub Documentation
Hunyuan 3D Tripo AI v3 Luma Genie Meshy AI v3 Meshy AI

NeRF Scene Generation

Generate 3D NeRF scenes from text.

STEPS:
  1. Clone repository and install dependencies
  2. Download model weights
  3. Enter detailed scene descriptions
  4. Generate NeRF representations
  5. Review lighting and view consistency
  6. Export for visualization or use

Research and Development

Use DreamFusion for 3D research.

STEPS:
  1. Set up research environment
  2. Experiment with score distillation parameters
  3. Generate NeRFs for research
  4. Analyze quality and view consistency
  5. Document findings and improvements
  6. Contribute to research community
Free Completely free
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Q

Is DreamFusion free?

A

Yes, DreamFusion is completely free to use.

Q

Does DreamFusion have an API?

A

No, DreamFusion does not appear to offer a public API.

Q

What is DreamFusion best for?

A

DreamFusion is best for Research. Generates high-quality 3D NeRF (Neural Radiance Field) representations from text prompts using score distillation sampling, a technique that leverages pre-trained 2D diffusion models for 3D generation. Pioneering NeRF-based text-to-3D generation using score distillation, representing a significant advancement in 3D content creation from text without requiring 3D training datasets.

Q

What platforms does DreamFusion support?

A

DreamFusion supports local.

Q

Is DreamFusion open source?

A

Yes, DreamFusion is open source. You can access the source code on GitHub at https://github.com/ashawkey/stable-dreamfusion.

Q

How do I get started with DreamFusion?

A

Clone the Stable DreamFusion repository from GitHub. Follow installation instructions and set up dependencies. Download model weights and configure environment. Enter detailed text prompts describing 3D scenes or objects. Generate NeRF representations using score distillation. Review 3D quality, lig...

Q

How do I create 3D models with DreamFusion?

A

DreamFusion generates 3D models from text descriptions. Enter detailed prompts describing the 3D object you want to create.

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