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Description
Backflip is an innovative 3D design platform powered by AI, allowing users to quickly turn their creative ideas into physical components. By simply describing a concept, sketching, or uploading an image, the AI generates a 3D model at the click of a button. These models are suitable for 3D printing in various materials, including metal, carbon fiber, and plastic. The platform encourages the creation, assembly, remixing, and sharing of 3D designs, which enhances collaboration and sparks innovation among users. Backflip is dedicated to transforming the early stages of the design process, leveraging their expertise in 3D printing technology. Their AI-driven design tools are specifically tailored to speed up hardware innovation and development, enabling users to create as quickly as their imagination allows. They offer multiple subscription options, ranging from free to pro and enterprise, each with varying access levels and features. Currently, Backflip is providing full access to its platform at no cost, without the need for a credit card, making it accessible for everyone interested in 3D design. This initiative encourages a broader audience to explore and experiment with 3D modeling, fostering a creative community.
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
Integrations
No details available.
Integrations
No details available.
Pricing Details
Free
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
Backflip AI
Founded
2013
Website
backflip.ai/
Vendor Details
Company Name
DreamFusion
Website
dreamfusion3d.github.io