SoraPix vs Drag Your GAN

SoraPix AI is an advanced AI-powered picture generator tool that allows users to create their dream anime girl with ease. This online platform presents a user-friendly interface where anime enthusiasts can explore a variety of distinctive built-in models. Through simple steps, you can select, customize, and dress up your own anime girlfriend.

SoraPix AI provides high-quality image features with a subscription that includes access to special items called Gems. These Gems can be used to unlock various wardrobe features and to access more advanced anime creation tools. With the option to try for free, subscribers also get the benefit of 4K HD image quality, a faster service, and priority in the creation queue.

The platform emphasizes that it does not generate adult, age-restricted, sexually-oriented, or pornographic content. For those who run out of Gems or want to expand their creative options, Gem Bundles are available for purchase. In case of inquiries or issues, the support team is reachable for hassle-free assistance.

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.