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Description
Text2Mesh generates intricate geometric and color details across various source meshes, guided by a specified text prompt. The results of our stylization process seamlessly integrate unique and seemingly unrelated text combinations, effectively capturing both overarching semantics and specific part-aware features. Our system, Text2Mesh, enhances a 3D mesh by predicting colors and local geometric intricacies that align with the desired text prompt. We adopt a disentangled representation of a 3D object, using a fixed mesh as content integrated with a learned neural network, which we refer to as the neural style field network. To alter the style, we compute a similarity score between the style-describing text prompt and the stylized mesh by leveraging CLIP's representational capabilities. What sets Text2Mesh apart is its independence from a pre-existing generative model or a specialized dataset of 3D meshes. Furthermore, it is capable of processing low-quality meshes, including those with non-manifold structures and arbitrary genus, without the need for UV parameterization, thus enhancing its versatility in various applications. This flexibility makes Text2Mesh a powerful tool for artists and developers looking to create stylized 3D models effortlessly.
Description
SpAItial is an innovative AI platform dedicated to the creation and implementation of Spatial Foundation Models (SFMs), a groundbreaking category of generative AI systems that excel in generating and interpreting 3D environments while maintaining physical realism and spatial intelligence. Unlike conventional models that independently generate images or text, SpAItial's advanced technology works directly with 3D structures from the beginning, effectively capturing aspects such as geometry, materials, lighting, and physics to create immersive and interactive worlds. Its premier model, Echo-2, possesses the remarkable ability to convert a single image into a fully navigable, photorealistic 3D scene using cutting-edge techniques like Gaussian splatting, which allows users to explore and render environments in real time. This platform is designed with a robust, physically grounded comprehension of space-time, enabling the AI to analyze how objects are situated, interact, and develop within a given environment, eschewing the disjointed outputs typical of traditional generative AI. This innovative methodology not only mitigates the inconsistencies often found in standard generative AI systems but also facilitates a more precise and realistic simulation of environments, paving the way for exciting new applications in virtual reality and beyond.
API Access
Has API
API Access
Has API
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Integrations
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Pricing Details
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Free Trial
Free Version
Pricing Details
Free
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
Text2Mesh
Website
threedle.github.io/text2mesh/
Vendor Details
Company Name
spAItial
Country
United States
Website
app.spaitial.ai/