Publications (10)
Generating High-Resolution Fashion Model Images Wearing Custom Outfits
Gökhan Yildirim, Nikolay Jetchev, Roland Vollgraf +1
Visualizing an outfit is an essential part of shopping for clothes. Due to the combinatorial aspect of combining fashion articles, the available images are limited to a pre-determi…
ClipMatrix: Text-controlled Creation of 3D Textured Meshes
Nikolay Jetchev
If a picture is worth thousand words, a moving 3d shape must be worth a million. We build upon the success of recent generative methods that create images fitting the semantics of…
The Conditional Analogy GAN: Swapping Fashion Articles on People Images
Nikolay Jetchev, Urs Bergmann
We present a novel method to solve image analogy problems : it allows to learn the relation between paired images present in training data, and then generalize and generate images…
Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail Generation
Nikolay Jetchev, Gökhan Yildirim, Christian Bracher +1
Attention is a general reasoning mechanism than can flexibly deal with image information, but its memory requirements had made it so far impractical for high resolution image gener…
Transform the Set: Memory Attentive Generation of Guided and Unguided Image Collages
Nikolay Jetchev, Urs Bergmann, Gökhan Yildirim
Cutting and pasting image segments feels intuitive: the choice of source templates gives artists flexibility in recombining existing source material. Formally, this process takes a…
First Order Generative Adversarial Networks
Calvin Seward, Thomas Unterthiner, Urs Bergmann +2
GANs excel at learning high dimensional distributions, but they can update generator parameters in directions that do not correspond to the steepest descent direction of the object…
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
Nikolay Jetchev, Urs Bergmann, Gokhan Yildirim
Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization. However, learning complicated image representations requires compute-int…
GANosaic: Mosaic Creation with Generative Texture Manifolds
Nikolay Jetchev, Urs Bergmann, Calvin Seward
This paper presents a novel framework for generating texture mosaics with convolutional neural networks. Our method is called GANosaic and performs optimization in the latent noise…
Texture Synthesis with Spatial Generative Adversarial Networks
Nikolay Jetchev, Urs Bergmann, Roland Vollgraf
Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper…
Learning Texture Manifolds with the Periodic Spatial GAN
Urs Bergmann, Nikolay Jetchev, Roland Vollgraf
This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise dis…