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Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

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

Screenshots View All

Screenshots View All

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

Product Features

Product Features

Alternatives

Alternatives

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