GET3D (Nvidia)
GET3D is NVIDIA’s AI model that generates detailed, textured 3D meshes directly from 2D images. Ideal for gaming, animation, and virtual world creation—no 3D scanning required.
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About GET3D
What is GET3D?
GET3D is an advanced generative model developed by NVIDIA that creates high-quality, textured 3D meshes directly from 2D image collections. Unlike traditional 3D modeling pipelines that require scans, sensors, or CAD tools, GET3D leverages deep learning to generate complex 3D objects—ready to use in animation, games, and virtual production.
Ein Sprung in der 3D-Inhaltserstellung
GET3D wurde mithilfe von Adversarial Learning und differenzierbarem Rendering trainiert und kann vielfältige Objekte mit realistischen Texturen und Geometrien erzeugen. Es erzeugt Meshes mit hoher Wiedergabetreue, beliebiger Topologie und komplexen Materialdetails und schließt so die Lücke zwischen KI und produktionsreifen 3D-Assets.
How GET3D Works
Latent Space Representation
GET3D generates two distinct latent codes: one for shape (geometry) and another for texture. These are used to produce a signed distance field (SDF) and a texture field that define the 3D mesh and surface appearance.
Mesh Extraction & Texturing
Using DMTet (Deep Marching Tetrahedra), GET3D converts the SDF into a triangular mesh. Then, it queries the texture field to paint the mesh with detailed color and material features.
Training with 2D Discriminators
GET3D wird mit 2D-Bildern und Silhouetten mit gegnerischen Verlusten trainiert. Differenzierbares Rendering ermöglicht dem Modell die Rückpropagierung von Fehlern aus dem Bildraum in den 3D-Raum und ermöglicht so Lernen ohne explizite 3D-Überwachung.
Key Capabilities of GET3D
High-Quality 3D Meshes
GET3D generates textured 3D objects with fine details such as headlights, seams, fur, and reflections—making it suitable for animation and simulation tasks.
Arbitrary Topology Support
Unlike many earlier models, GET3D can generate complex, non-rigid shapes across a wide range of categories including animals, vehicles, furniture, shoes, and human avatars.
Entwirrte Kontrolle von Form und Textur
GET3D separates geometry and texture into distinct latent codes. Users can independently manipulate either aspect to achieve greater control in asset generation.
Latente Code-Interpolation
By interpolating between latent vectors, GET3D enables smooth transitions and morphing between shapes and textures. This feature is useful for animation, asset variation, and design iteration.
Text-Guided Generation
Incorporating CLIP-based directional loss (as seen in StyleGAN-NADA), GET3D supports text-guided shape generation. Users can fine-tune outputs using natural language prompts for creative control.
Material- und Lichteffekte
When combined with DIBR++ (a hybrid renderer), GET3D can also simulate materials and lighting effects in an unsupervised fashion, enhancing realism in renders.
Anwendungen von GET3D
Gaming & Interactive Media
Game developers can rapidly generate character models, props, and environments with consistent geometry and texture, significantly reducing manual modeling time.
Animation und Filmproduktion
GET3D ermöglicht schnelles Prototyping stilisierter oder fotorealistischer Assets mit flexiblen Designvariationen und direktem Export in Rendering-Pipelines.
Virtual Reality & Metaverse
Ideal for VR creators, GET3D provides a scalable way to populate virtual spaces with high-quality 3D content—without the need for traditional scanning or modeling.
3D E-Commerce und digitale Zwillinge
Retailers and industrial designers can use GET3D to visualize products in 3D from catalog images, enhancing interactive shopping and simulation workflows.
Forschungshighlights
- Disentangled Geometry and Texture: Independent control of mesh shape and surface appearance.
- Adversarial Image-Based Training: No 3D labels or models required—just image collections.
- Latent Code Interpolation: Smooth transitions between different shapes and styles.
- Hohe Kompatibilität: Gibt standardmäßige Mesh-Formate aus, die mit Blender, Unity, Unreal und anderen Engines kompatibel sind.
Project Origins & Contributors
GET3D is the result of collaborative research between NVIDIA, the University of Toronto, and the Vector Institute, presented at NeurIPS 2022. It builds on prior work like DMTet, EG3D, and DIBR++, further advancing 3D generative modeling.
Resources and Access
- GET3D GitHub & Codebase
- Research Paper PDF & arXiv
- Zitat- und BibTeX–Informationen auf der Projektseite verfügbar