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Found 11 papers, 7 papers with code
ClipMatrix: Text-controlled Creation of 3D Textured Meshes
If a picture is worth thousand words, a moving 3d shape must be worth a million.
Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail Generation
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 generation.
Transform the Set: Memory Attentive Generation of Guided and Unguided Image Collages
Cutting and pasting image segments feels intuitive: the choice of source templates gives artists flexibility in recombining existing source material.
Generating High-Resolution Fashion Model Images Wearing Custom Outfits
Visualizing an outfit is an essential part of shopping for clothes.
Conditional Image Generation
Vocal Bursts Intensity Prediction
Unlabeled Disentangling of GANs with Guided Siamese Networks
In addition, we illustrate that simple guidance functions we use in UD-GAN-G allow us to directly capture the desired variations in the data.
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization.
First Order Generative Adversarial Networks
To formally describe an optimal update direction, we introduce a theoretical framework which allows the derivation of requirements on both the divergence and corresponding method for determining an update direction, with these requirements guaranteeing unbiased mini-batch updates in the direction of steepest descent.
Ranked #2 on
Image Generation
on LSUN Bedroom 64 x 64
GANosaic: Mosaic Creation with Generative Texture Manifolds
This paper presents a novel framework for generating texture mosaics with convolutional neural networks.
The Conditional Analogy GAN: Swapping Fashion Articles on People Images
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 that correspond to the relation, but were never seen in the training set.
Learning Texture Manifolds with the Periodic Spatial GAN
Second, we show that the image generation with PSGANs has properties of a texture manifold: we can smoothly interpolate between samples in the structured noise space and generate novel samples, which lie perceptually between the textures of the original dataset.
Texture Synthesis with Spatial Generative Adversarial Networks
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.
