This paper addresses the problem of interpolating visual textures. We formulate the problem of texture interpolation by requiring (1) by-example controllability and (2) realistic and smooth interpolation among an arbitrary number of texture samples. To solve it we propose a neural network trained simultaneously on a reconstruction task and a generation task, which can project texture examples onto a latent space where they can be linearly interpolated and reprojected back onto the image domain, thus ensuring both intuitive control and realistic results. We show several additional applications including texture brushing and texture dissolve, and show our method outperforms a number of baselines according to a comprehensive suite of metrics as well as a user study.
Recently, generative adversarial networks (GANs) @cite_21 @cite_7 @cite_46 @cite_2 have shown improved realism in image synthesis and translation tasks @cite_32 @cite_42 @cite_41 . GANs have also been used directly for texture synthesis @cite_51 @cite_30 @cite_4 , however they were limited to a single texture they were trained on. A recent approach dubbed PSGAN @cite_57 learns to synthesize a collection of textures present in a single photograph, making it more general and applicable to texture interpolation; it does not, however, allow for user control by specifying which textures are synthesized.
@cite_30 @cite_4 @cite_7 @cite_41 @cite_21 @cite_42 @cite_32 @cite_57 @cite_2 @cite_46 @cite_51
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This paper proposes a new GAN-based approach for example-based non-stationary texture synthesis. It can cope with challenging textures, which, to our knowledge, no other existing method can handle. We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples. Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F : Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach. This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (, 2014), and call this technique Periodic Spatial GAN (PS-GAN). The PSGAN has several novel abilities which surpass the current state of the art in texture synthesis. First, we can learn multiple textures, periodic or non-periodic, from datasets of one or more complex large images. Second, we show that the image generation with PS-GANs 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. We make multiple experiments which show that PSGANs can flexibly handle diverse texture and image data sources, and the method is highly scalable and can generate output images of arbitrary large size. This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic texture, or photos to artistic paintings. With adversarial training, we obtain quality comparable to recent neural texture synthesis methods. As no optimization is required at generation time, our run-time performance (0.25 M pixel images at 25 Hz) surpasses previous neural texture synthesizers by a significant margin (at least 500 times faster). We apply this idea to texture synthesis, style transfer, and video stylization.