We are going to go into more detail about GAN in this article because people are interested in learning more about it. Deep generation models, such as generative adversarial networks (GANs), have shown remarkable efficiency in producing random photorealistic images. In real world applications, the ability to control over the combined visual input is essential for learning-based methods of image synthesis. For example, social media users may want to modify a person’s or animal’s body placement, shape, expression, and pose in a candid photo, professional media editors may need to quickly sketch scene layouts. movie-specific and car designers may want to reshape their designs interactively.
Multiple of generative image
To meet these various user goals, an ideal controlled image synthesis technique should possess the following characteristics. 1) Flexibility: You must be able to control a variety of spatial characteristics, such as the location, attitude, expression, and arrangement of crafted objects or creatures. 2) Accuracy: You must be able to manage spatial features very accurately. 3. Generality: It must be applicable to several types of objects without being specific to any of them. This work tries to fully satisfy all of these traits, whereas previous works only fully met one or two of them. Most of the older techniques were based on supervised learning, which uses manually annotated data or older 3D models to train GANs in a controlled manner.
Recently, text-guided image synthesis has gained attention. As a result, these techniques occasionally handle a limited number of spatial features or provide the user with limited editing power. They should also apply to new types of objects. However, the text guide must increase its adaptability and accuracy when changing spatial features. It cannot be used, for example, to relocate an object to a specific number of pixels. The authors of this paper discuss a robust but underutilized interactive point-based manipulation for flexible, precise, and comprehensive control capability of GANs. The goal is to shift the control points in the direction of the proper target point by clicking as many control points and target points as you want on the image.
The method that best resembles our scenario studies drag-based manipulation. Users have control over a variety of partial properties thanks to this point-based manipulation, which is independent of object categories. Compared to that problem, the one presented in this article has two additional challenges: they account for handling multiple points, which their approach has a hard time doing, and they also require checkpoints to accurately reach the points. goal, what your approach fails to achieve. do.
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Source: vtt.edu.vn