1430results about "Texturing/coloring" patented technology

Systems and methods for image processing are described. Embodiments of the present disclosure, via a multi-modal encoder of an image processing apparatus, encodes a text prompt to obtain a text embedding. A color encoder of the image processing apparatus encodes a color prompt to obtain a color embedding. A diffusion prior model of the image processing apparatus generates an image embedding based on the text embedding and the color embedding. A latent diffusion model of the image processing apparatus generates an image based on the image embedding, where the image includes an element from the text prompt and a color from the color prompt.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that edit digital images using combinations of speech input and gesture interactions. For instance, in some embodiments, the disclosed systems receive speech input from a client device displaying a digital image within a graphical user interface, the digital image portraying an object. Additionally, the disclosed systems detect, via the graphical user interface, one or more gesture interactions with respect to the object of the digital image. Based on the speech input, the disclosed systems determine an edit for the object of the digital image indicated by the one or more gesture interactions. Further, the disclosed systems modify the object within the digital image using the edit indicated by the one or more gesture interactions.

Provided is a real scene image editing method based on hierarchically classified text guidance, including: firstly selecting a hierarchical multi-label text classification model and hierarchically classify an input style description text; obtaining a latent vector of an indoor scene image and dividing the latent vector; training latent space residual mappers which are divided into four groups for generating details of a layout, an object, an attribute, and a color in the scene image, and selectively training a mapping model with a secondary word obtained by a text classification model; inputting a tertiary word obtained by the text classification model to a contrastive language-image pre-training (CLIP) network and controlling training of the mapping network by utilizing a CLIP loss; hierarchically inputting the latent vector to the mapping network to obtain a bias vector, summing the bias vector with an original vector for inputting to the StyleGAN to obtain an edited image.

An example computer-implemented image processing method includes receiving a digital image including a face area of a first subject and a background area, receiving a reference image of a second subject, modifying the digital image based on the reference image utilizing a generative neural network to create a modified digital image in which a feature of the second subject is transferred to the first subject, and combining the face area of the first subject and the background area from the digital image with the feature area from the modified digital image to create a composite modified digital image that includes data representing the feature of the second subject transferred to the first subject from the modified digital image and data representing the face area of the first subject and the background area from the digital image.

The present disclosure generally relates to creating and editing avatars, and navigating avatar selection interfaces. In some examples, an avatar feature user interface includes a plurality of feature options that can be customized to create an avatar. In some examples, different types of avatars can be managed for use in different applications. In some examples, an interface is provided for navigating types of avatars for an application.

The present disclosure generally relates to creating and editing avatars, and navigating avatar selection interfaces. In some examples, an avatar feature user interface includes a plurality of feature options that can be customized to create an avatar. In some examples, different types of avatars can be managed for use in different applications. In some examples, an interface is provided for navigating types of avatars for an application.

An avatar editing environment is disclosed that allows users to create custom avatars for use in online games and other applications. Starting with a blank face the user can add, rescale and position different elements (e.g., eyes, nose, mouth) on the blank face. The user can also change the shape of the avatar's face, the avatar's skin color and the color of all the elements. In some implementations, touch input and gestures can be used to manually edit the avatar. Various controls can be used to create the avatar, such as controls for resizing, rotating, positioning, etc. The user can choose between manual and automatic avatar creation. The avatar editing environment can be part of a framework that is available to applications. One or more elements of the avatar can be animated.

Generative image synthesis conditioned on label prompts includes receiving a label prompt comprising a mapping of labels to portions of an image to be synthesized. At least some of the labels in the label prompt correspond to an object to be included in the image to be synthesized. It further includes, based on the label prompt, generating, using a generative model, a synthesized image corresponding to the label prompt.

A data processing system implements techniques for generating personalized content using a brand kit. The system receives a natural language prompt to generate content in a design application on the client device of a user and analyzes the prompt to determine whether the user intends to apply a brand kit to the generated content. The system automatically generates a brand kit for the user if one does not already exist and applies the brand kit to content generated using one or more generative models to create personalized content. The system includes a prompt generation unit that generates a plurality of model-specific prompts to the one or more generative models to cause the one or more generative models to create the personalized content.

Systems and methods train and apply a specialized encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The specialized encoder neural network includes an input layer, a feature extraction layer, and a bottleneck layer positioned after the feature extraction layer. The projection process includes providing an input image to the encoder and producing, by the encoder, a latent space representation of the input image. Producing the latent space representation includes extracting a feature vector from the feature extraction layer, providing the feature vector lo the bottleneck layer as input, and producing the latent space representation as output. The latent space representation produced by the encoder is provided as input to the GAN, which generates an output image based upon the latent space representation.

Thematic map preparation method and apparatus, a storage medium and an electronic device are provided. The method includes: preparing a map style master based on an aimed application field; generating metadata information corresponding to the map style master, wherein the metadata information includes a mapping relationship between each of the geographical units in the map style master and the initial color identification corresponding thereto, and the metadata information further includes location information about the legend in the map style master; processing original input data, and acquiring an attribute color identification of each geographical unit in the original input data; and replacing the initial color identification corresponding to each of the geographical units in the map style master with the attribute color identification, and updating the legend, so as to obtain a thematic map of the original input data when aimed at the application field.

A method, apparatus, non-transitory computer readable medium, and system for image generation includes obtaining an input image and an input prompt, where the input image depicts an object and the input prompt describes a lighting condition for the object, generating relighted image features based on the input image and the input prompt, where the relighted image features represent the object with the lighting condition, and generating a synthetic image based on the relighted image features, where the synthetic image depicts the object with the lighting condition.

Systems and methods for multi-modal image editing are provided. In one aspect, a system and method for multi-modal image editing includes identifying an image, a prompt identifying an element to be added to the image, and a mask indicating a first region of the image for depicting the element. The system then generates a partially noisy image map that includes noise in the first region and image features from the image in a second region outside the first region. A diffusion model generates a composite image map based on the partially noisy image map and the prompt. In some cases, the composite image map includes the target element in the first region that corresponds to the mask.

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.

An infrared image processing method, apparatus and device, and a storage medium are provided. The infrared image processing method includes: acquiring an infrared image; performing target detection on the infrared image to determine target objects within the infrared image and obtaining a target detection result that include a category of each of the target objects; determining a target object of interest from the target objects; determining a contour area of the target object of interest; performing true-color processing on the contour area to highlight the target object of interest. By highlighting the target object of interest in the infrared image through true-color processing, the target object of interest can be displayed more prominently with different colors and textures according to its category, so that the target object of interest can be presented more clearly in the infrared image clearer and easier for the human eye to observe and recognize.

In some embodiments, an electronic device generates an automatically-generated visual media using one or more recognized concepts extracted from a prompt inputted by a user. The recognized concepts include personalized template subjects and / or prompt suggestions. While displaying the user interface including the recognized concepts, the electronic device receives one or more inputs to modify the recognized concepts. The electronic device generates multiple variants of the automatically-generated visual content using the one or more recognized concepts. The electronic device adds an automatically-generated visual content to a content entry field of an application, different than the automatically-generated visual media application, without opening the automatically-generated visual media application. The electronic device applies a visual effect to content that is generated using an artificial intelligence model. The electronic device displays visual information corresponding to an artificial intelligence model. The electronic device displays an animation including displaying a user interface with high dynamic range luminance.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that recolors a digital design according to colors of a digital image and further generates an enhanced recolored digital design. In particular, in some embodiments, the disclosed systems identify a digital image for recoloring a digital design and recolors the digital design utilizing a color affine transformation algorithm to generate a recolored digital design. Further, in some embodiments, the disclosed systems generate the enhanced recolored digital design by transforming one or more colors of the recolored digital design to be within a range of the colors of the digital image utilizing a convex hull projection method. Moreover, in some embodiments, the disclosed systems further enhance the recolored digital design utilizing a contrast enhancement algorithm to modify luminescence values.

An image processing method, performed by a computer device, comprising: extracting sketch texture features at multiple scales from a sketch image; extracting image noise features at multiple scales from preset noise; determining color guide information corresponding to the sketch image; encoding, for each scale, a noise feature based on a sketch texture feature and the color guide information to obtain multi-scale image features; and performing multi-scale decoding on these image features to obtain a colored image comprising a sketch texture corresponding to the sketch image and a color based on the color guide information. A related training method and apparatus are also provided to develop models for this image processing technique.

An improved system architecture uses a Generative Adversarial Network (GAN) including a specialized generator neural network to generate multiple resolution output images. The system produces a latent space representation of an input image. The system generates a first output image at a first resolution by providing the latent space representation of the input image as input to a generator neural network comprising an input layer, an output layer, and a plurality of intermediate layers and taking the first output image from an intermediate layer, of the plurality of intermediate layers of the generator neural network. The system generates a second output image at a second resolution different from the first resolution by providing the latent space representation of the input image as input to the generator neural network and taking the second output image from the output layer of the generator neural network.

A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining a pattern prompt and a text image, where the pattern prompt describes a visual pattern and the text image depicts text, generating a pattern image based on the pattern prompt, where the pattern image depicts the visual pattern, and generating a patterned text image based on the pattern image and the pattern prompt.

Systems and methods generate paired image data comprising synthesized eyeglass reflections and use the paired image data to train a machine learning model for reflection removal. A training dataset is generated that includes image pairs. Each image pair comprises a first version of a face image with eyeglasses not having a reflection and a second version of the face image with eyeglasses having a reflection. A first image pair in the training dataset is generated by: obtaining a first face image with eyeglasses not having a reflection, obtaining a reflection image, and generating a composite image using the first face image and the reflection image. Once generated, the training dataset is used to train a machine learning model to provide a trained machine learning model that performs reflection removal on input face images with eyeglass reflections.

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital designs utilizing a diffusion neural network to preserve readability and design composition while modifying image content background images and design assets. In some embodiments, the disclosed systems access a text prompt defining visual attributes of a digital design. Furthermore, the disclosed systems generate a modified text prompt by replacing chromatic information within the text prompt. Additionally, the disclosed systems determine an adaptive strength for a diffusion neural network from the text prompt. Also, the disclosed systems generate a modified digital design utilizing the diffusion neural network to process the modified text prompt according to the adaptive strength.

Systems and methods are provided for image editing, and more particularly, for harmonizing background images with text. Embodiments of the present disclosure obtain an image including text and a region overlapping the text. In some aspects, the text includes a first color. Embodiments then select a second color that contrasts with the first color, and generate a modified image including the text and a modified region using a machine learning model that takes the image and the second color as input. The modified image is generated conditionally, so as to include the second color in a region corresponding to the text.

Apparatuses, systems, and techniques to process luminance and / or radiance values of one or more images from one or more cameras using one or more neural networks to perform a machine vision task. In at least one embodiment, one or more neural networks determine detection difficulty levels of objects within the one or more images and performs a machine vision task based on the determined detection difficulty levels of objects within images associated with that ask.

Embodiments for colorizing images, including pixel-format images and vector-format images, include obtaining input data including an outline image and a color hint. The color hint includes a colored portion corresponding to a region of the outline image. Then, embodiments process the input data to obtain control guidance for an image generator using an outline encoder. Embodiments generate a synthesized image based on the control guidance using an image generator. The synthesized image depicts an object having a shape based on the outline image and a color based on the color hint. In some cases, embodiments also transfer the colors from the synthesized image to a base vector image to produce a colorized vector image.

The current document is directed to an infrastructure-as-code (“IaC”) cloud-infrastructure-management service or system including a graph-based user interface that displays, and that provides for generating and editing, cloud-infrastructure-specification-and-configuration files. The IaC cloud-infrastructure-management service or system processes a set of infrastructure-specification-and-configuration files to identify specified resources and relationships between resources. The resources and relationships are then stored as a graph, along with various indices that can be used for quickly searching the graph for relationships and resources of interest. A graph-based user interface allows users to view portions of the graph, navigate through the graph, search for particular resources and relationships, and edit the infrastructure-specification-and-configuration files.

Methods and systems provide generative apparel recommendations within a conversational platform. In one embodiment, the system generates, using an visual AI (artificial intelligence) model, one or more new images depicting a person in an input image with one or more different apparel items and / or hair styles than depicted in the input image. The system inputs the one or more AI generated new images into a visual AI model where the visual AI model being trained to identify apparel patterns and accessory, apparel and / or clothing item components. The system identifies, by the visual AI model, apparel patterns from the input one or more AI generated new images and extracts accessory, apparel and / or clothing item components. The system curates a set of apparel items based on at least the extracted accessory, apparel and / or clothing item components. The system provides for display, via a user interface, the curated set of apparel items.

A high dynamic range editing system is configured to generate visualizations to aide digital image editing in both high dynamic ranges and standard dynamic ranges. In a first example, the visualization is generated as a histogram. In a second example, the visualization is generated to indicate high dynamic range capabilities. In a third example, the visualization is generated to indicate ranges of luminance values within a digital image. In a fourth example, the visualization is generated as a point curve that defines a mapping between detected luminance values from a digital image and output luminance values over both a standard dynamic range and a high dynamic range. In a fifth example, the visualization is generated as a preview to convert pixels from the digital image in a high dynamic range into a standard dynamic range.