APOB AI vs Drag Your GAN

APOB Creator AI is an innovative platform designed to generate a unique AI persona with exceptional ease and precision. Aspiring creators and professionals can craft a personalized digital twin using just one photo, opening the gates to content creation like never before. With APOB Creator AI, users can produce high-quality digital content, ranging from static images to dynamic videos, featuring their AI-generated persona.

The app offers instant creation capabilities, allowing the digital replica to be adapted to various scenarios – befitting keynote presentations, vibrant social media posts, virtual meetups, or tutorial videos. There is no need for a credit card to start, making it free to create the initial portrait.

APOB Creator AI is lauded as a formidable competitor to similar tools and has been praised for its superior quality and ease of use. It prides itself on enabling users to enhance their brand and presence with exclusive AI models, paving the way for innovative content creation in marketing, entertainment, and communication.

In the realm of synthesizing visual content to meet users' needs, achieving precise control over pose, shape, expression, and layout of generated objects is essential. Traditional approaches to controlling generative adversarial networks (GANs) have relied on manual annotations during training or prior 3D models, often lacking the flexibility, precision, and versatility required for diverse applications.

In our research, we explore an innovative and relatively uncharted method for GAN control – the ability to "drag" specific image points to precisely reach user-defined target points in an interactive manner (as illustrated in Fig.1). This approach has led to the development of DragGAN, a novel framework comprising two core components:

Feature-Based Motion Supervision: This component guides handle points within the image toward their intended target positions through feature-based motion supervision.

Point Tracking: Leveraging discriminative GAN features, our new point tracking technique continuously localizes the position of handle points.

DragGAN empowers users to deform images with remarkable precision, enabling manipulation of the pose, shape, expression, and layout across diverse categories such as animals, cars, humans, landscapes, and more. These manipulations take place within the learned generative image manifold of a GAN, resulting in realistic outputs, even in complex scenarios like generating occluded content and deforming shapes while adhering to the object's rigidity.

Our comprehensive evaluations, encompassing both qualitative and quantitative comparisons, highlight DragGAN's superiority over existing methods in tasks related to image manipulation and point tracking. Additionally, we demonstrate its capabilities in manipulating real-world images through GAN inversion, showcasing its potential for various practical applications in the realm of visual content synthesis and control.