Fig. 1. TileGAN can synthesize large-scale textures with rich details. We show aerial images at different levels of detail generated using our framework, which allows for interactive texture editing. Our results contain a broad diversity of features at multiple scales and can be several hundreds of megapixels in size.We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required. We build on recent generative adversarial networks and propose two extensions in this paper. First, we propose an algorithm to combine outputs of GANs trained on a smaller resolution to produce a large-scale plausible texture map with virtually no boundary artifacts. Second, we propose a user interface to enable artistic control. Our quantitative and qualitative results showcase the generation of synthesized high-resolution maps consisting of up to hundreds of megapixels as a case in point.

Figure 1. InsetGAN applications. Our full-body human generator is able to generate reasonable bodies at state-of-the-art resolution (1024×1024px) (a). However, some artifacts appear in the synthesized results, most visibly in extremities and faces. We make use of a second, specialized generator to seamlessly improve the face region (b). We can also use a given face as an input for unconditional generation of bodies (c). Furthermore, we can select both specific faces and bodies and compose them in a seamlessly merged output (d).

Figure 1. InsetGAN applications. Our full-body human generator is able to generate reasonable bodies at state-of-the-art resolution (1024×1024px) (a). However, some artifacts appear in the synthesized results, most visibly in extremities and faces. We make use of a second, specialized generator to seamlessly improve the face region (b). We can also use a given face as an input for unconditional generation of bodies (c). Furthermore, we can select both specific faces and bodies and compose them in a seamlessly merged output (d).

Figure 1. We propose VIVE3D, a novel method that creates a powerful personalized 3D-aware generator using a low number of selected images of a target person. Given a new video of that person, we can faithfully modify several facial attributes as well as the camera viewpoint of the head crop. Finally, we seamlessly composite the edited face with the source frame in a temporally and spatially consistent manner, while retaining a plausible composition with the static components of the frame outside of the generator's region. The dotted squares in the center frame denote the reference regions for the three different camera poses in the column below.