Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture

This paper addresses the problem of interpolating visual textures. We formulate this problem 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 projected back onto the image domain, thus ensuring both intuitive control and realistic results. We show our method outperforms a number of baselines according to a comprehensive suite of metrics as well as a user study. We further show several applications based on our technique, which include texture brush, texture dissolve, and animal hybridization.Comment: Accepted to CVPR'1

Distributed texture-based terrain synthesis

Terrain synthesis is an important field of Computer Graphics that deals with the generation of 3D landscape models for use in virtual environments. The field has evolved to a stage where large and even infinite landscapes can be generated in realtime. However, user control of the generation process is still minimal, as well as the creation of virtual landscapes that mimic real terrain. This thesis investigates the use of texture synthesis techniques on real landscapes to improve realism and the use of sketch-based interfaces to enable intuitive user control

Faithful completion of images of scenic landmarks using internet images

Abstract—Previous works on image completion typically aim to produce visually plausible results rather than factually correct ones. In this paper, we propose an approach to faithfully complete the missing regions of an image. We assume that the input image is taken at a well-known landmark, so similar images taken at the same location can be easily found on the Internet. We first download thousands of images from the Internet using a text label provided by the user. Next, we apply two-step filtering to reduce them to a small set of candidate images for use as source images for completion. For each candidate image, a co-matching algorithm is used to find correspondences of both points and lines between the candidate image and the input image. These are used to find an optimal warp relating the two images. A completion result is obtained by blending the warped candidate image into the missing region of the input image. The completion results are ranked according to combination score, which considers both warping and blending energy, and the highest ranked ones are shown to the user. Experiments and results demonstrate that our method can faithfully complete images

FrameBreak: Dramatic Image Extrapolation by Guided Shift-Maps

Master'sMASTER OF ENGINEERIN

Biggerpicture: data-driven image extrapolation using graph matching

Filling a small hole in an image with plausible content is well studied. Extrapolating an image to give a distinctly larger one is much more challenging---a significant amount of additional content is needed which matches the original image, especially near its boundaries. We propose a data-driven approach to this problem. Given a source image, and the amount and direction(s) in which it is to be extrapolated, our system determines visually consistent content for the extrapolated regions using library images. As well as considering low-level matching, we achieve consistency at a higher level by using graph proxies for regions of source and library images. Treating images as graphs allows us to find candidates for image extrapolation in a feasible time. Consistency of subgraphs in source and library images is used to find good candidates for the additional content; these are then further filtered. Region boundary curves are aligned to ensure consistency where image parts are joined using a photomontage method. We demonstrate the power of our method in image editing applications

State of the Art in Example-based Texture Synthesis

International audienceRecent years have witnessed significant progress in example-based texture synthesis algorithms. Given an example texture, these methods produce a larger texture that is tailored to the user's needs. In this state-of-the-art report, we aim to achieve three goals: (1) provide a tutorial that is easy to follow for readers who are not already familiar with the subject, (2) make a comprehensive survey and comparisons of different methods, and (3) sketch a vision for future work that can help motivate and guide readers that are interested in texture synthesis research. We cover fundamental algorithms as well as extensions and applications of texture synthesis

Accurate and discernible photocollages

There currently exist several techniques for selecting and combining images from a digital image library into a single image so that the result meets certain prespecified visual criteria. Image mosaic methods, first explored by Connors and Trivedi[18], arrange library images according to some tiling arrangement, often a regular grid, so that the combination of images, when viewed as a whole, resembles some input target image. Other techniques, such as Autocollage of Rother et al.[78], seek only to combine images in an interesting and visually pleasing manner, according to certain composition principles, without attempting to approximate any target image. Each of these techniques provide a myriad of creative options for artists who wish to combine several levels of meaning into a single image or who wish to exploit the meaning and symbolism contained in each of a large set of images through an efficient and easy process. We first examine the most notable and successful of these methods, and summarize the advantages and limitations of each. We then formulate a set of goals for an image collage system that combines the advantages of these methods while addressing and mitigating the drawbacks. Particularly, we propose a system for creating photocollages that approximate a target image as an aggregation of smaller images, chosen from a large library, so that interesting visual correspondences between images are exploited. In this way, we allow users to create collages in which multiple layers of meaning are encoded, with meaningful visual links between each layer. In service of this goal, we ensure that the images used are as large as possible and are combined in such a way that boundaries between images are not immediately apparent, as in Autocollage. This has required us to apply a multiscale approach to searching and comparing images from a large database, which achieves both speed and accuracy. We also propose a new framework for color post-processing, and propose novel techniques for decomposing images according to object and texture information