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What is GET3D?

We develop a three-dimensional signed distance field (SDF) alongside a textured field using two latent codes. To extract a 3D surface mesh from the SDF, we utilize DMTet, sampling the texture field at surface points for color information. Our training process includes adversarial losses centered on 2D images, employing a rasterization-based differentiable renderer to generate both RGB visuals and silhouettes. To differentiate between real and generated inputs, we introduce two distinct 2D discriminators—one dedicated to RGB images and the other to silhouettes. The entire system is structured to enable end-to-end training. As various industries shift towards creating expansive 3D virtual environments, the necessity for scalable tools capable of generating large volumes of high-quality and diverse 3D content becomes increasingly evident. Our research aims to develop robust 3D generative models that produce textured meshes, facilitating their seamless integration into 3D rendering engines for immediate deployment in a range of applications. This strategy not only addresses the challenge of scalability but also opens up new avenues for innovative uses in fields like virtual reality and gaming. Moreover, by enhancing the quality and diversity of 3D content, we aim to push the boundaries of creativity and interactivity within these immersive environments.

What is DragGAN?

Crafting visual content that meets user specifications often requires a significant level of adaptability and accuracy when it comes to controlling the pose, shape, expression, and layout of the generated components. Conventional approaches increase the manageability of generative adversarial networks (GANs) by depending on labeled training datasets or existing 3D models, which often lack the required flexibility, precision, and responsiveness. In this study, we investigate a robust yet somewhat overlooked method for manipulating GANs that enables users to "drag" specific points within an image to precisely reach target locations through interactive participation, as demonstrated in Fig.1. Our proposed method, DragGAN, consists of two main elements: a feature-based motion supervision system that directs the handle point to the desired position, and an innovative point tracking technique that leverages the distinguishing features of GANs to continuously monitor the locations of the handle points. By using DragGAN, individuals have the ability to adjust images with remarkable accuracy in guiding pixel movements, which leads to a more intuitive and user-focused design experience. This innovative approach not only broadens creative opportunities but also allows users to more effectively realize their intended visual results. Ultimately, DragGAN represents a significant advancement in the field of interactive image manipulation, fostering a new era of creativity and precision in visual content creation.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

NVIDIA

Company Location

United States

Company Website

nv-tlabs.github.io/GET3D/

Company Facts

Organization Name

DragGAN

Date Founded

2023

Company Website

vcai.mpi-inf.mpg.de/projects/DragGAN/

Categories and Features

Categories and Features

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