NVIDIA Get3D

What is NVIDIA Get3D?

Who created NVIDIA Get3D?

GET3D, developed by researchers from NVIDIA, the University of Toronto, and the Vector Institute, was presented at NeurIPS 2022. The team behind GET3D includes Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler. This innovative technology focuses on generating high-quality, textured 3D shapes directly from 2D images, addressing the demand for detailed 3D assets in various industries.

What is NVIDIA Get3D used for?

• High-Quality 3D Assets: Generates 3D textured shapes with intricate details directly from 2D images
• Advanced Disentanglement: Achieves clear separation between geometry and texture allowing creative flexibility
• Text-Guided Shape Generation: Offers capability to create shapes based on textual prompts enhancing user interactivity
• End-to-End Trainable Model: Utilizes adversarial losses and differentiable rendering for an efficient training process
• Unsupervised Material Generation: Produces materials and view-dependent lighting effects without supervision
• High-quality 3D textured shapes generation from 2D images
• Advanced disentanglement of geometry and texture for creative flexibility
• Text-guided shape generation based on textual prompts
• End-to-end trainable model utilizing adversarial losses and differentiable rendering
• Unsupervised material generation including lighting effects
• Diverse shapes generation with arbitrary topology, high-quality geometry, and texture
• Latent code interpolation for smooth transitions between different shapes
• Generating similar shapes with slight differences locally
• Unsupervised material generation and view-dependent lighting effects
• Text-guided shape generation with user-provided text prompts
• Advanced disentanglement between geometry and texture for creative flexibility
• Text-guided shape generation based on textual prompts for enhanced user interactivity
• End-to-end trainable model with adversarial losses and differentiable rendering for efficient training process
• Unsupervised material generation including view-dependent lighting effects
• Generating similar looking shapes with slight differences locally
• Unsupervised material generation for meaningful view-dependent lighting effects
• Text-guided shape generation allowing users to provide text prompts for meaningful shape generation
• High-Quality 3D Assets Generation: Creates 3D textured shapes with intricate details directly from 2D images
• Text-guided shape generation using textual prompts for enhanced interactivity
• Unsupervised material generation without supervision
• Generating diverse shapes with arbitrary topology, high-quality geometry, and texture
• Smooth transition generation between different shapes
• Local perturbation of latent code to generate similar shapes with slight differences
• Unsupervised material generation for materials and view-dependent lighting effects
• Text-guided shape generation by finetuning the 3D generator based on user-provided texts

Who is NVIDIA Get3D for?

• Gaming industry
• Film industry
• Virtual reality industry
• Gaming industry professionals
• Film Industry Professionals
• Virtual reality industry professionals

How to use NVIDIA Get3D?

To use GET3D by Nvidia, follow these steps:

1. Access the GET3D generative model that creates high-quality 3D textured meshes directly from 2D images.
2. Utilize advancements in differentiable rendering, surface modeling, and generative adversarial networks to produce complex 3D shapes with rich textures.
3. Enjoy various features such as high-quality 3D asset generation, advanced disentanglement of geometry and texture, text-guided shape creation, an end-to-end trainable model, and unsupervised material generation.
4. Learn about GET3D through FAQs including its development by researchers from NVIDIA, the University of Toronto, and the Vector Institute, and its presentation at NeurIPS 2022.
5. Explore the model's capability to generate diverse shapes like cars, chairs, animals, and human characters with detailed textures and complex topology.
6. Experiment with text-guided shape generation by inputting text prompts to guide the creation of 3D shapes, enhancing user interactivity.
7. For further insights, refer to the paper "GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images" by Jun Gao et al. presented at NeurIPS 2022.
8. Stay updated on new features and advancements related to GET3D through the provided citations of related works.