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Average Ratings 0 Ratings
Description
Seed3D 1.0 serves as a foundational model pipeline that transforms a single image input into a 3D asset ready for simulation, encompassing closed manifold geometry, UV-mapped textures, and material maps suitable for physics engines and embodied-AI simulators. This innovative system employs a hybrid framework that integrates a 3D variational autoencoder for encoding latent geometry alongside a diffusion-transformer architecture, which meticulously crafts intricate 3D shapes, subsequently complemented by multi-view texture synthesis, PBR material estimation, and completion of UV textures. The geometry component generates watertight meshes that capture fine structural nuances, such as thin protrusions and textural details, while the texture and material segment produces high-resolution maps for albedo, metallic properties, and roughness that maintain consistency across multiple views, ensuring a lifelike appearance in diverse lighting conditions. Remarkably, the assets created using Seed3D 1.0 demand very little post-processing or manual adjustments, making it an efficient tool for developers and artists alike. Users can expect a seamless experience with minimal effort required to achieve professional-quality results.
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.
API Access
Has API
No
API Access
Has API
No
Integrations
No details available.
Integrations
No details available.
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
ByteDance
Founded
2012
Country
China
Website
seed.bytedance.com/en/seed3d
Vendor Details
Company Name
Text2Mesh
Website
threedle.github.io/text2mesh/