Adobe, Inc.

Czech Technical University in Prague

Michal Lukac

Elya Shechtman

Nathan Aaron Carr

Kalyan Krishna Sunkavalli

Paul John Asente

Sohrab Amirghodsi

Jingwan Lu

Oliver Wang

Matthew David Fisher

Vineet Batra

Ankit Phogat

Xin Sun

David P Simons

Jakub Fiser

Souymodip Chakraborty

Ondrej Jamriska

Connelly Barnes

Xiaoyi Wang

Chang Liu

Wilmot Wei-Mau Li

Eric Joel Stollnitz

Daniel Max Kaufman

Geoffrey Oxholm

Vidya Narayanan

Amanda Paige Ghassaei

Ning Yu

Ramiz Sheikh

Mahyar Najibikohnehshahri

Sangeeta Varma

Shayan Chandrashekar

Systems and methods for image processing are described. Embodiments of the present disclosure receive a raster image depicting a radial color gradient; compute an origin point of the radial color gradient based on an orthogonality measure between a color gradient vector at a point in the raster image and a relative position vector between the point and the origin point; construct a vector graphics representation of the radial color gradient based on the origin point; and generate a vector graphics image depicting the radial color gradient based on the vector graphics representation.

Reconstructing concentric radial gradients

Systems and methods for image processing are described. Embodiments of the present disclosure include receiving a raster image depicting a radial color gradient; computing a radial disk model for the radial color gradient, wherein the radial disk model defines a plurality of disks with centers aligned in a same direction; constructing a vector graphics representation of the radial color gradient based on the radial disk model; and generating a vector graphics image depicting the radial color gradient based on the vector graphics representation.

Reconstructing general radial gradients

An object folding tool is leveraged in a digital medium environment. A two-dimensional (2D) representation of an unfolded object is obtained, and visual cues indicating folds for transforming the unfolded object into a folded object are detected. Based on the detected visual cues, a shape of the folded object is determined, and a three-dimensional (3D) representation of the folded object having the determined shape is generated. In one or more implementations, the 2D representation of the unfolded object and the 3D representation of the folded object are displayed concurrently on a display device.

Object folding tool

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating a modified digital image by identifying patch matches within a digital image utilizing a Gaussian mixture model. For example, the systems described herein can identify sample patches and corresponding matching portions within a digital image. The systems can also identify transformations between the sample patches and the corresponding matching portions. Based on the transformations, the systems can generate a Gaussian mixture model, and the systems can modify a digital image by replacing a target region with target matching portions identified in accordance with the Gaussian mixture model.

Performing patch matching guided by a transformation gaussian mixture model

Methods and systems disclosed herein relate generally to systems and methods for transforming document elements in response to modifications to a layout of a document. A layout-modification application identifies, from a first document having a first document layout, a first set of measurements of a document element and a first location of the document element within the first document. Based on an aspect-ratio difference between the first document layout and a second document layout, the layout-modification application selects a set of transformation rules that specify, for the document element, a second set of measurements and a second location within a second document. To select the particular set of transformation rules, the layout-modification application uses the determined aspect-ratio difference. The layout-modification application applies the selected set of transformation rules to the document element.

Transforming document elements for modified document layouts

Certain aspects involve video inpainting via confidence-weighted motion estimation. For instance, a video editor accesses video content having a target region to be modified in one or more video frames. The video editor computes a motion for a boundary of the target region. The video editor interpolates, from the boundary motion, a target motion of a target pixel within the target region. In the interpolation, confidence values assigned to boundary pixels control how the motion of these pixels contributes to the interpolated target motion. A confidence value is computed based on a difference between forward and reverse motion with respect to a particular boundary pixel, a texture in a region that includes the particular boundary pixel, or a combination thereof. The video editor modifies the target region in the video by updating color data of the target pixel to correspond to the target motion interpolated from the boundary motion.

Video inpainting via confidence-weighted motion estimation

Generating modified digital images by identifying digital image patch matches utilizing a Gaussian mixture model

An example method for neural network based interpolation of image textures includes training a global encoder network to generate global latent vectors based on training texture images, and training a local encoder network to generate local latent tensors based on the training texture images. The example method further includes interpolating between the global latent vectors associated with each set of training images, and interpolating between the local latent tensors associated with each set of training images. The example method further includes training a decoder network to generate reconstructions of the training texture images and to generate an interpolated texture based on the interpolated global latent vectors and the interpolated local latent tensors. The training of the encoder and decoder networks is based on a minimization of a loss function of the reconstructions and a minimization of a loss function of the interpolated texture.

Texture interpolation using neural networks

Certain embodiments involve generating one or more of appearance guide and a positional guide and using one or more of the guides to synthesize a stylized image or animation. For example, a system obtains data indicating a target image and a style exemplar image. The system generates an appearance guide, a positional guide, or both from the target image and the style exemplar image. The system uses one or more of the guides to transfer a texture or style from the style exemplar image to the target image.

Generating a stylized image or stylized animation by matching semantic features via an appearance guide, a segmentation guide, and/or a temporal guide

Techniques for using deep learning to facilitate patch-based image inpainting are described. In an example, a computer system hosts a neural network trained to generate, from an image, code vectors including features learned by the neural network and descriptive of patches. The image is received and contains a region of interest (e.g., a hole missing content). The computer system inputs it to the network and, in response, receives the code vectors. Each code vector is associated with a pixel in the image. Rather than comparing RGB values between patches, the computer system compares the code vector of a pixel inside the region to code vectors of pixels outside the region to find the best match based on a feature similarity measure (e.g., a cosine similarity). The pixel value of the pixel inside the region is set based on the pixel value of the matched pixel outside this region.

Deep patch feature prediction for image inpainting

Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map. The image modification module produces a manipulated image by manipulating the original image under global symmetry constraints imposed by the global symmetry association map.

Image modification using detected symmetry

The present disclosure is directed toward systems and methods for image patch matching. In particular, the systems and methods described herein sample image patches to identify those image patches that match a target image patch. The systems and methods described herein probabilistically accept image patch proposals as potential matches based on an oracle. The oracle is computationally inexpensive to evaluate but more approximate than similarity heuristics. The systems and methods use the oracle to quickly guide the search to areas of the search space more likely to have a match. Once areas are identified that likely include a match, the systems and methods use a more accurate similarity function to identify patch matches.

Image patch matching using probabilistic sampling based on an oracle

Certain embodiments involve generating an appearance guide, a segmentation guide, and a positional guide and using one or more of the guides to synthesize a stylized image or animation. For example, a system obtains data indicating a target and a style exemplar image and generates a segmentation guide for segmenting the target image and the style exemplar image and identifying a feature of the target image and a corresponding feature of the style exemplar image. The system generates a positional guide for determining positions of the target feature and style feature relative to a common grid system. The system generates an appearance guide for modifying intensity levels and contrast values in the target image based on the style exemplar image. The system uses one or more of the guides to transfer a texture of the style feature to the corresponding target feature.

Certain embodiments involve using labels to track high-frequency offsets for patch-matching. For example, a processor identifies an offset between a first source image patch and a first target image patch. If the first source image patch and the first target image patch are sufficiently similar, the processor updates a data structure to include a label specifying the offset. The processor associates, via the data structure, the first source image patch with the label. The processor subsequently selects certain high-frequency offsets, including the identified offset, from frequently occurring offsets in the data structure. The processor uses these offsets to identify a second target image patch, which is located at the identified offset from a second source image patch. The processor associates, via the data structure, the second source image patch with the identified offset based on a sufficient similarity between the second source image patch and the second target image patch.

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San Jose, CA, United States of America

Michal Lukac