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Description
Creating visual content that aligns with user requirements often necessitates a high degree of flexibility and precision in managing the pose, shape, expression, and arrangement of the generated elements. Traditional methods enhance the controllability of generative adversarial networks (GANs) by relying on manually labeled training datasets or pre-existing 3D models, which frequently fall short in terms of flexibility, accuracy, and adaptability. In this research, we explore a powerful yet relatively underutilized technique for controlling GANs, which allows users to "drag" specific points in an image to accurately reach designated target locations through interactive engagement, as illustrated in Fig.1. Our proposed solution, DragGAN, comprises two primary components: first, a feature-based motion supervision system that guides the handle point toward the intended position; and second, an innovative point tracking method that utilizes the discriminative features of GANs to continuously identify the handle points' locations. With DragGAN, users gain the capability to manipulate images with exceptional precision in directing pixel movements, thereby facilitating a more intuitive and user-centered design process. This approach not only enhances creative possibilities but also empowers users to achieve their desired visual outcomes more effectively.
Description
By incorporating image conditioning techniques alongside a prompt-based editing method, we offer users innovative ways to manipulate 3D synthesis, paving the way for various creative possibilities. Magic3D excels in generating high-quality 3D textured mesh models based on textual prompts. It employs a coarse-to-fine approach that utilizes both low- and high-resolution diffusion priors to effectively learn the 3D representation of the desired content. Moreover, Magic3D produces 3D content with 8 times the resolution supervision compared to DreamFusion, while also operating at twice the speed. Once a rough model is created from an initial text prompt, we can alter elements of the prompt and subsequently fine-tune both the NeRF and 3D mesh models, resulting in an enhanced high-resolution 3D mesh. This versatility not only enhances user creativity but also streamlines the workflow for producing detailed 3D visualizations.
API Access
Has API
API Access
Has API
Integrations
No details available.
Integrations
No details available.
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
DragGAN
Founded
2023
Website
vcai.mpi-inf.mpg.de/projects/DragGAN/
Vendor Details
Company Name
Magic3D
Website
research.nvidia.com/labs/dir/magic3d/