Adobe, Inc.

Jose Ignacio Echevarria Vallespi

Bernard Kerr

Zhe Lin

Elya Shechtman

Jingwan Lu

Stephen J DiVerdi

Paul John Asente

Jianming Zhang

Zhaowen Wang

Baldo Antonio Faieta

Duygu Ceylan Aksit

Saeid Motiian

Kalyan Krishna Sunkavalli

Sourabh Gupta

David P Simons

Yang Zhou

Dingzeyu Li

He Zhang

Mrinal Kumar Sharma

Cameron Smith

Gourav Singhal

Stephen Diverdi

Samarth Gulati

Pramod Srinivasan

Yinglan Ma

Mainak Biswas

Syed Mohd Mehadi

Hema Susmita Padala

Dmitry Baranovskiy

Adrian Cristian Brojbeanu

Radomir Mech

Franck Dernoncourt

Gavin Stuart Peter Miller

Vishwa Vinay

Yilin Wang

Li-Yi Wei

Zhili Chen

Nedim Lipka

Yijun Li

Michele Saad

Nirmal Kumawat

Yannick Hold-Geoffroy

Stefano Petrangeli

Daichi Ito

Zhifei Zhang

Saikat Chakrabarty

Zijun Wei

Alex Charles Filipkowski

Kyle Olszewski

Qi Sun

Peter Evan O'Donovan

Lauren Dest

William F Kraus

Shikhar Garg

Yulun Zhang

Xin Sun

Daniel John Vogel

Nathan Douglas Baldwin

Tenell Glen Rhodes, Jr

Frederick Mandia

Dustin Ground

Meredith Payne Stotzner

Xin Wang

Craig Kaplan

Spyridon Ampanavos

Anurag Singhal

Shubham Seth

Manisha Gupta

Koki Madono

Matthew May

Jinjin Sun

Emelia May Hughes

Jaskirat Singh

Shaoyu Chen

Rachel Franz

Jane Little E

Jeya Maria Jose Valanarasu

Zegi Gu

Amirreza Shirani

Thamar I Solorio Martinez

Rohit Jain

Elya Shectman

Gerben Hettinga

Stephen Di Verdi

Sarah Garanganao Almeda

Rebecca Ilene Milman

Rinat Abdrashitov

Cheryl Lao

Methods and systems disclosed herein relate generally to increasing visibility of pixel patterns of an image. The system includes a pattern-detection application accessing an image depicting an object. The pattern-detection application determines a set of colors from the transformed image. The pattern-detection application identifies a set of pixels depicting a particular color of the set of colors. For the set of pixels depicting the particular color, the pattern-detection application converts an initial set of pixel values of the set of pixels at an initial color space to another set of pixel values that define the particular color of the set of pixels in another color space. The pattern-detection application modifies one or more values of the other set of pixel values to generate a modified set of pixel values. The modification includes causing the set of pixels visually indicate a simulated color that is different from the particular color.

Increasing visibility of pixel patterns in images

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and providing pose guides that are tailored to a context of a camera viewfinder stream. In particular, in one or more embodiments, the disclosed systems determine a context of the camera viewfinder stream and provide sample pose images that correspond with the determined context. In response to a selection of a sample pose image, the disclosed systems generate and display a pose guide that is customized to the proportions of a subject depicted in the camera viewfinder stream. The disclosed systems further iteratively modify portions of the generated pose guide to indicate that the subject depicted in the camera viewfinder stream is coming into alignment with the generated pose guide. The disclosed systems automatically capture a digital image when the subject is in total alignment with the generated pose guide.

Providing contextual augmented reality photo pose recommendations

Embodiments are disclosed for recommending fonts based on text inputs are described. In some embodiments, a method of recommending fonts includes receiving a selection of text, providing a representation of the selection of text to a font recommendation model, generating, by the font recommendation model, a prediction score for each of a plurality of fonts based on the representation of the selection of text, and returning at least one recommended font based on the prediction score for each of the plurality of fonts.

Context-aware font recommendation from text

The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a dual-branched neural network architecture to harmonize composite images. For example, in one or more implementations, the transformer-based harmonization system uses a convolutional branch and a transformer branch to generate a harmonized composite image based on an input composite image and a corresponding segmentation mask. More particularly, the convolutional branch comprises a series of convolutional neural network layers followed by a style normalization layer to extract localized information from the input composite image. Further, the transformer branch comprises a series of transformer neural network layers to extract global information based on different resolutions of the input composite image. Utilizing a decoder, the transformer-based harmonization system combines the local information and the global information from the corresponding convolutional branch and transformer branch to generate a harmonized composite image.

Harmonizing composite images utilizing a transformer neural network

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating harmonized digital images utilizing an object-to-object harmonization neural network. For example, the disclosed systems implement, and learn parameters for, an object-to-object harmonization neural network to combine a style code from a reference object with features extracted from a target object. Indeed, the disclosed systems extract a style code from a reference object utilizing a style encoder neural network. In addition, the disclosed systems generate a harmonized target object by applying the style code of the reference object to a target object utilizing an object-to-object harmonization neural network.

Object-to-object harmonization for digital images

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating painted digital images utilizing an intelligent painting process that includes progressive layering, sequential brushstroke guidance, and/or brushstroke regularization. For example, the disclosed systems utilize an image painting model to perform progressive layering to generate and apply digital brushstrokes in a progressive fashion for different layers associated with a background canvas and foreground objects. In addition, the disclosed systems utilize sequential brushstroke guidance to generate painted foreground objects by sequentially shifting through attention windows for regions of interest in a target digital image. Furthermore, the disclosed systems utilize brushstroke regularization to generate and apply an efficient brushstroke sequence to generate a painted digital image.

Generating digital paintings utilizing an intelligent painting pipeline for improved brushstroke sequences

In implementations of systems for accessible digital painting, a computing device implements a landmark system to receive input data describing a coordinate of a first type of user interaction in a user interface of a digital canvas. The landmark system determines that the coordinate of the first type of user interaction is within a threshold distance of a coordinate of a digital landmark that corresponds to a visual feature of a visual layer of the digital canvas. Feedback is generated that indicates the coordinate of the first type of user interaction is within the threshold distance of the coordinate of the digital landmark. Additional input data is received describing a coordinate of a second type of user interaction in the user interface based on the feedback. The landmark system generates a stroke of digital paint for display in the user interface based on the additional input data.

Systems for accessible digital painting

A method includes accessing a first object in a virtual environment, the first object representing a first asset at a first level of detail (LoD). A second object is generated to represent the first asset at a second LoD having decreased complexity. The method further includes determining a first importance value for the first asset and, based on the first importance value, selecting the first object to represent the first asset. Additionally, the method includes accessing a third object representing the second asset at the first LoD and generating a fourth object representing the second asset at the second LoD. The method further includes determining a second importance value, lower than the first importance value, for the second asset and selecting the fourth object to represent the second asset. The method further includes causing a client device to update a display of the virtual environment by transmitting the selected objects.

Delivering a virtual environment with dynamic level of detail per object

The present disclosure relates to systems that perform text-based palette searches that convert a text query into a color distribution and utilize the color distribution to identify relevant color palettes. More specifically, the disclosed systems receive a textual color palette search query and convert, utilizing a text-to-color model, the textual color palette search query into a color distribution. The disclosed systems determine, utilizing a palette scoring model, distance metrics between the color distribution and a plurality of color palettes in a color database by: identifying swatch matches between colors of the color distribution and unmatched swatches of the plurality of color palettes and determining distances between the colors of the color distribution and matched swatches of the plurality of color palettes. The disclosed systems return one or more color palettes of the plurality of color palettes in response to the textual color palette search query based on the distance metrics.

Text-based color palette searches utilizing text-to-color models

Methods, systems, and non-transitory computer readable media are disclosed for intelligently generating partially textured accessible images. In one or more embodiments, the disclosed systems generate or access a color texture map specific to a given color vision deficiency. For example, the disclosed systems generate a color texture map that divides a color space into one or more textured segment of colors and one or more complementary untextured segments of colors. The disclosed systems can utilize the color texture map to intelligently apply different textures to subsets of pixels that contribute to color vision deficiency confusion. For example, the disclosed system maps pixels from a color image to the color texture map to identify a first subset of pixels corresponding to the textured segment of colors. The disclosed system generates a partially textured accessible image by applying a texture to the first subset of pixels.

Partially texturizing color images for color accessibility

Embodiments of the technology described herein, are an intelligent system that aims to expedite a text design process by providing text design predictions interactively. The system works with a typical text design scenario comprising a background image and one or more text strings as input. In the design scenario, the text string is to be placed on top of the background. The textual design agent may include a location recommendation model that recommends a location on the background image to place the text. The textual design agent may also include a font recommendation model, a size recommendation model, and a color recommendation model. The output of these four models may be combined to generate draft designs that are evaluated as a whole (combination of color, font, and size) for the best designs. The top designs may be output to the user.

Textual design agent

A contrast ratio color picker system is described that generates, based on a selected color, a contrast ratio color picker that presents a filtered palette of colors satisfying a contrast ratio threshold, relative to the selected color. To do so, the contrast ratio color picker system computes a relative luminance value for the selected color and identifies a contrast ratio threshold for the digital content. The contrast ratio color picker system then selects a palette from a target color space and generates a filtered palette by masking pixels displaying colors of the target color space palette that fail to satisfy the contrast ratio threshold. The filtered palette is then output as part of the contrast ratio color picker, which enables application of colors selected from the filtered palette to digital content.

Contrast ratio color picker

A vectorized caricature avatar generator receives a user image from which face parameters are generated. Segments of the user image including certain facial features (e.g., hair, facial hair, eyeglasses) are also identified. Segment parameter values are also determined, the segment parameter values being those parameter values from a set of caricature avatars that correspond to the segments of the user image. The face parameter values and the segment parameter values are used to generate a caricature avatar of the user in the user image.

Vectorized caricature avatar generator

Embodiments of the present disclosure relate to computer storage, methods, and systems for the optimization of accessible color themes. Systems and methods are disclosed that leverage the use of confusion lines to identify and highlight relationships between colors that may be inaccessible (e.g., indistinguishable) for a person with a vision impairment, such as a color vision deficiency. In some embodiments, a graphical user interface is provided that, based on a selection of colors in a color wheel, visually indicates curves of confusion for each color in the selection of colors. Each curve of confusion visually indicates a confusion of colors for a type of vision impairment, such as a CVD.

Authoring and optimization of accessible color themes

Embodiments of the present invention provide systems, methods, and computer storage media for generating an animation of a talking head from an input audio signal of speech and a representation (such as a static image) of a head to animate. Generally, a neural network can learn to predict a set of 3D facial landmarks that can be used to drive the animation. In some embodiments, the neural network can learn to detect different speaking styles in the input speech and account for the different speaking styles when predicting the 3D facial landmarks. Generally, template 3D facial landmarks can be identified or extracted from the input image or other representation of the head, and the template 3D facial landmarks can be used with successive windows of audio from the input speech to predict 3D facial landmarks and generate a corresponding animation with plausible 3D effects.

Style-aware audio-driven talking head animation from a single image

Growing community of inventors

San Jose, CA, United States of America

Jose Ignacio Echevarria Vallespi