Instruct 3D-to-3D
High-fidelity text-guided conversion and editing of 3D scenes using iterative diffusion updates.
High-fidelity 3D textured mesh generation from 2D images for real-time gaming and digital twins.
NVIDIA GET3D (Generative Education of 3D objects) is a breakthrough generative model designed to synthesize high-quality 3D textured meshes from 2D image collections. Built on a foundation of latent-space GANs and differentiable surface modeling, GET3D allows developers and researchers to generate complex shapes with arbitrary topology and high-resolution textures in a single inference pass. Unlike traditional NeRF-based approaches that require per-scene optimization, GET3D outputs standard mesh formats (OBJ/STL) with material properties ready for integration into game engines like Unreal Engine 5 or Unity. As we move into 2026, GET3D has established itself as the architectural gold standard for procedural asset generation pipelines, enabling the creation of massive virtual environments without the manual labor traditionally required for 3D modeling. The system utilizes a differentiable renderer—specifically nvdiffrast—to bridge the gap between 2D supervision and 3D generation, ensuring that the generated models are not only visually realistic but also geometrically sound for physical simulations and industrial digital twins within the NVIDIA Omniverse ecosystem.
Uses a hybrid surface representation that allows for arbitrary topology and high-resolution geometry optimization.
High-fidelity text-guided conversion and editing of 3D scenes using iterative diffusion updates.
High-Fidelity Shading-Guided 3D Asset Generation from Sparse 2D Inputs
High-Quality Single Image to 3D Generation using 2D and 3D Diffusion Priors
Edit 3D scenes and NeRFs with natural language instructions while maintaining multi-view consistency.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Generates both geometry and texture simultaneously through a shared latent space.
A modular primitive for high-performance differentiable rendering that enables backpropagation through 3D meshes.
Generates 2D texture maps directly from the 3D surface representation.
Smoothly transitions between different 3D shapes by traversing the latent manifold.
Capable of modeling objects with holes and complex interior structures (e.g., hollowed-out furniture).
Can be trained on diverse datasets like ShapeNet or custom synthetic data.
Manual creation of background props takes thousands of artist hours.
Registry Updated:2/7/2026
Assign physics colliders automatically.
Robots need diverse 3D objects to train grasping algorithms.
Creating diverse urban environments requires high-quality building and vehicle assets.