Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
We create a three-dimensional signed distance field (SDF) and a textured field using two latent codes. DMTet is employed to derive a 3D surface mesh from the SDF, and we sample the texture field at the surface points to obtain color information. Our training incorporates adversarial losses focused on 2D images, specifically utilizing a rasterization-based differentiable renderer to produce both RGB images and silhouettes. To distinguish between genuine and generated inputs, we implement two separate 2D discriminators—one for RGB images and another for silhouettes. The entire framework is designed to be trainable in an end-to-end manner. As various sectors increasingly transition towards the development of expansive 3D virtual environments, the demand for scalable tools that can generate substantial quantities of high-quality and diverse 3D content has become apparent. Our research endeavors to create effective 3D generative models capable of producing textured meshes that can be seamlessly integrated into 3D rendering engines, thereby facilitating their immediate application in various downstream uses. This approach not only addresses the scalability challenge but also enhances the potential for innovative applications in virtual reality and gaming.
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.
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
No
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
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
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
NVIDIA
Country
United States
Website
nv-tlabs.github.io/GET3D/
Vendor Details
Company Name
ByteDance
Founded
2012
Country
China
Website
seed.bytedance.com/en/seed3d