Add to ORKG3D GANs have the ability to generate latent codes for entire 3D volumes rather than only 2D images. These models offer desirable features like high-quality geometry and multi-view consistency, but, unlike their 2D counterparts, complex semantic image editing tasks for 3D GANs have only been partially explored. To address this problem, we propose LatentSwap3D, a semantic edit approach based on latent space discovery that can be used with any off-the-shelf 3D or 2D GAN model and on any dataset. LatentSwap3D relies on identifying the latent code dimensions corresponding to specific attributes by feature ranking using a random forest classifier. It then performs the edit by swapping the selected dimensions of the image being edited with the one...
We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-si...
Generative Neural Radiance Fields (GNeRF) based 3D-aware GANs have demonstrated remarkable capabilit...
The latent space of GANs contains rich semantics reflecting the training data. Different methods pro...
Prior work has extensively studied the latent space structure of GANs forunconditional image synthes...
This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create...
The high-quality images yielded by generative adversarial networks (GANs) have motivated investigati...
Inverting a Generative Adversarial Network (GAN) facilitates a wide range of image editing tasks usi...
This work presents an easy-to-use regularizer for GAN training, which helps explicitly link some axe...
Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkabl...
While the quality of GAN image synthesis has improved tremendously in recent years, our ability to c...
Deep neural networks have recently been used to edit images with great success, in particular for fa...
Recent advancements in real image editing have been attributed to the exploration of Generative Adve...
High-quality, diverse, and photorealistic images can now be generated by unconditional GANs (e.g., S...
Semantic editing of images is the fundamental goal of computer vision. Although deep learning method...
The recent GAN inversion methods have been able to successfully invert the real image input to the c...
We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-si...
Generative Neural Radiance Fields (GNeRF) based 3D-aware GANs have demonstrated remarkable capabilit...
The latent space of GANs contains rich semantics reflecting the training data. Different methods pro...
Prior work has extensively studied the latent space structure of GANs forunconditional image synthes...
This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create...
The high-quality images yielded by generative adversarial networks (GANs) have motivated investigati...
Inverting a Generative Adversarial Network (GAN) facilitates a wide range of image editing tasks usi...
This work presents an easy-to-use regularizer for GAN training, which helps explicitly link some axe...
Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkabl...
While the quality of GAN image synthesis has improved tremendously in recent years, our ability to c...
Deep neural networks have recently been used to edit images with great success, in particular for fa...
Recent advancements in real image editing have been attributed to the exploration of Generative Adve...
High-quality, diverse, and photorealistic images can now be generated by unconditional GANs (e.g., S...
Semantic editing of images is the fundamental goal of computer vision. Although deep learning method...
The recent GAN inversion methods have been able to successfully invert the real image input to the c...
We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-si...
Generative Neural Radiance Fields (GNeRF) based 3D-aware GANs have demonstrated remarkable capabilit...
The latent space of GANs contains rich semantics reflecting the training data. Different methods pro...