DragGAN，AI生成图像不同的角度
(资料图片仅供参考)
与SD不同，我认为这是AI比较好的应用，DragGAN可以帮助美术工作者调整角度绘画。
我们可以期待6月的开源，DragGAN如果妥善利用，将为美术带来质变。他在保留创作者创作的基础上，提高了生产力，我们能用更小的代价进行更多的尝试。
但我们依旧要小心使用，避免造成SD如今的滥用行为，我们应该尽全力探索技术，而不是想办法依靠开源代码信息差去获得不道德的收益
以下为原文摘要：[2305.10973] Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold (arxiv.org)
Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model, which often lack flexibility, precision, and generality. In this work, we study a powerful yet much less explored way of controlling GANs, that is, to "drag" any points of the image to precisely reach target points in a user-interactive manner, as shown in Fig.1. To achieve this, we propose DragGAN, which consists of two main components: 1) a feature-based motion supervision that drives the handle point to move towards the target position, and 2) a new point tracking approach that leverages the discriminative generator features to keep localizing the position of the handle points. Through DragGAN, anyone can deform an image with precise control over where pixels go, thus manipulating the pose, shape, expression, and layout of diverse categories such as animals, cars, humans, landscapes, etc. As these manipulations are performed on the learned generative image manifold of a GAN, they tend to produce realistic outputs even for challenging scenarios such as hallucinating occluded content and deforming shapes that consistently follow the object's rigidity. Both qualitative and quantitative comparisons demonstrate the advantage of DragGAN over prior approaches in the tasks of image manipulation and point tracking. We also showcase the manipulation of real images through GAN inversion.
