GET3D is an impressive generative model developed by NVIDIA Toronto AI Lab, designed to produce high-quality 3D textured shapes learned from images. Accepted to NeurIPS, this groundbreaking tool addresses the growing need for scalable content creation tools in industries that are increasingly relying on modeling massive 3D virtual worlds. With a focus on quantity, quality, and diversity of 3D content, GET3D aims to provide performant 3D generative models that seamlessly integrate with 3D rendering engines and can be readily used in various applications.
Features of GET3D
- Generative Model: GET3D utilizes cutting-edge techniques from differentiable surface modeling, differentiable rendering, and 2D Generative Adversarial Networks to generate explicit textured 3D meshes with complex topology, rich geometric details, and high-fidelity textures.
- Wide Range of Objects: This powerful tool can generate a diverse range of 3D textured meshes, including cars, chairs, animals, motorbikes, human characters, and buildings. It offers significant improvements over previous methods in terms of quality and variety.
- Dual Latent Codes: GET3D generates a 3D Signed Distance Field (SDF) and a texture field using two latent codes. The SDF is extracted into a 3D surface mesh using DMTet, while the texture field is queried at surface points to obtain colors, resulting in highly realistic textures.
- Adversarial Training: The model is trained using adversarial losses defined on 2D images. A differentiable renderer based on rasterization is employed to obtain RGB images and silhouettes. Two 2D discriminators, one for RGB images and the other for silhouettes, classify inputs as real or fake. This end-to-end trainable approach ensures consistent improvements in the generated 3D textured meshes.
Price
GET3D is available for free, making it an accessible tool for developers and 3D content creators. With its advanced capabilities and high-quality results, it offers exceptional value for users in need of a reliable generative model for 3D content generation.
Conclusion
GET3D is an impressive achievement in the field of 3D generative modeling. Developed by NVIDIA Toronto AI Lab and accepted to NeurIPS, it provides a solution to the growing demand for scalable content creation tools for virtual worlds. With its ability to generate explicit textured 3D meshes with complex topology, rich geometric details, and high-fidelity textures, GET3D offers a significant improvement over previous methods. Its wide range of object generation, dual latent codes, and adversarial training approach make it a powerful and versatile tool for developers and 3D content creators. Considering its free availability, GET3D is a highly recommended option for those seeking to create high-quality 3D content efficiently.
