GET3DNVIDIA
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About
We generate a 3D SDF and a texture field via two latent codes. We utilize DMTet to extract a 3D surface mesh from the SDF and query the texture field at surface points to get colors. We train with adversarial losses defined on 2D images. In particular, we use a rasterization-based differentiable renderer to obtain RGB images and silhouettes. We utilize two 2D discriminators, each on RGB image, and silhouette, respectively, to classify whether the inputs are real or fake. The whole model is end-to-end trainable. As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performant 3D generative models that synthesize textured meshes which can be directly consumed by 3D rendering engines, thus immediately usable in downstream applications.
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About
SpAItial is an AI platform focused on building and deploying Spatial Foundation Models (SFMs), a new class of generative AI systems designed to create and understand 3D environments with physical realism and spatial awareness. Unlike traditional models that generate pixels or text independently, SpAItial’s technology operates directly on 3D structures, capturing geometry, materials, lighting, and physics from the outset to produce coherent, interactive worlds. Its flagship model, Echo-2, can transform a single image into a fully explorable, photorealistic 3D scene using techniques like Gaussian splatting, enabling users to navigate and render environments in real time. It is built around a physically grounded understanding of space-time, allowing AI to reason about how objects exist, interact, and evolve within an environment rather than producing disconnected outputs. This approach reduces inconsistencies common in traditional generative AI and enables more accurate simulation.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Anyone seeking a generative model of high quality 3D textured shapes learned from images
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Audience
Developers and creators building immersive 3D, AR/VR, or robotics applications who need AI that can generate and reason about realistic spatial environments from minimal input
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationNVIDIA
United States
nv-tlabs.github.io/GET3D/
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Company InformationspAItial
United States
app.spaitial.ai/
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Integrations
No info available.
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Integrations
No info available.
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