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Description
3D-Agent is an innovative 3D modeling application powered by artificial intelligence, designed to seamlessly integrate with Blender, allowing users to create 3D models based on textual descriptions. It employs a sophisticated multi-agent AI system that orchestrates various models to interpret your scene, design geometry, generate Blender Python scripts, and visually confirm outcomes at each phase of the process.
In contrast to other AI-driven 3D model creators that produce triangle meshes often needing extensive refinement, 3D-Agent interacts directly with Blender's native Python API, yielding refined quad topology that is immediately suitable for subdivision, UV mapping, and animation rigging.
Core features include:
- The ability to convert text into 3D models with clean topology.
- An AI that is aware of and can comprehend existing objects within your viewport.
- Automation of workflows such as bulk renaming, compositing setups, and export configurations.
- Compatibility with Blender 3.0 and above on both Mac and Windows systems.
- Options for exporting in formats like OBJ, FBX, GLB, USDZ, and STL.
This tool is utilized by game developers, architects, and 3D artists for quick prototyping, architectural visualizations, and asset creation. Additionally, the free tier of the service allows for 15 model generations each month, making it accessible for newcomers and professionals alike. With its powerful capabilities, 3D-Agent is poised to transform the landscape of 3D modeling and design.
Description
Recent advancements in the realm of text-to-image synthesis have emerged from diffusion models that have been trained on vast amounts of image-text pairs. To successfully transition this methodology to 3D synthesis, it would necessitate extensive datasets of labeled 3D assets alongside effective architectures for denoising 3D information, both of which are currently lacking. In this study, we address these challenges by leveraging a pre-existing 2D text-to-image diffusion model to achieve text-to-3D synthesis. We propose a novel loss function grounded in probability density distillation that allows a 2D diffusion model to serve as a guiding principle for the optimization of a parametric image generator. By implementing this loss in a DeepDream-inspired approach, we refine a randomly initialized 3D model, specifically a Neural Radiance Field (NeRF), through gradient descent to ensure its 2D renderings from various angles exhibit a minimized loss. Consequently, the 3D representation generated from the specified text can be observed from multiple perspectives, illuminated with various lighting conditions, or seamlessly integrated into diverse 3D settings. This innovative method opens new avenues for the application of 3D modeling in creative and commercial fields.
API Access
Has API
API Access
Has API
Screenshots View All
No images available
Integrations
Blender
Pricing Details
$10
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
3D-Agent
Founded
2025
Country
United States
Website
3d-agent.com
Vendor Details
Company Name
DreamFusion
Website
dreamfusion3d.github.io