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.
Un salto en la creación de contenido 3D
Entrenado mediante aprendizaje adversarial y renderizado diferenciable, GET3D puede producir diversos objetos con texturas y geometría realistas. Genera mallas de alta fidelidad, topología arbitraria y detalles de materiales intrincados, acortando la distancia entre la IA y los recursos 3D listos para producción.
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 se entrena con imágenes 2D y siluetas con pérdidas adversarias. El renderizado diferenciable permite que el modelo retropropague errores del espacio de la imagen al espacio 3D, lo que facilita el aprendizaje sin supervisión 3D explícita.
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.
Control desenredado de forma y textura
GET3D separates geometry and texture into distinct latent codes. Users can independently manipulate either aspect to achieve greater control in asset generation.
Interpolación de código latente
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 y efectos de iluminación
When combined with DIBR++ (a hybrid renderer), GET3D can also simulate materials and lighting effects in an unsupervised fashion, enhancing realism in renders.
Aplicaciones de GET3D
Gaming & Interactive Media
Game developers can rapidly generate character models, props, and environments with consistent geometry and texture, significantly reducing manual modeling time.
Producción de animación y cine
GET3D permite la creación rápida de prototipos de activos estilizados o fotorrealistas con variación de diseño flexible y exportación directa a canales de renderizado.
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.
Comercio electrónico 3D y gemelos digitales
Retailers and industrial designers can use GET3D to visualize products in 3D from catalog images, enhancing interactive shopping and simulation workflows.
Aspectos destacados de la investigación
- 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.
- Alta compatibilidad: Genera formatos de malla estándar compatibles con Blender, Unity, Unreal y otros motores.
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
- Información sobre citas y BibTeX disponible en la página del proyecto