1005results about "Texturing/coloring" patented technology

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.

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.

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.

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.

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.

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.

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.

In an approach for providing an immersive interview system with real-time interactive feedback, a processor presents a simulated environment of an interview based on an interview type and an interview format selected by a user. A processor collects a set of data regarding at least one performance related factor and nonperformance related factor for a length of the interview. A processor evaluates the set of data collected to detect a deficiency associated with the at least one performance related factor of the user. A processor detects the deficiency associated with the at least one performance related factor of the user. A processor outputs a notification to the user, wherein the notification is a recommendation explaining to the user a suggested change to improve the interview skill during the interview.

In various examples, infrared image data (e.g., frames of an infrared (IR) video feed) may be colorized by transferring color statistics from an RGB image with an overlapping field of view, by modifying one or more dimensions of an encoded representation of a generated RGB image, and / or otherwise. For example, segmentation may be applied to the IR and RGB image data, and the one or more colors or statistics may be transferred from a segmented region of the RGB image data to a corresponding segmented region of the IR image data. In some embodiments, synthesized RGB image data may be fined tuned by transferring color or color statistic(s) from corresponding real RGB image data, and / or by modifying one or more dimensions of an encoded representation of the synthesized RGB image data.

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.

An image processing device according to the present invention is usable together with a head-mounted display device including a first image sensor configured to obtain distance information, a second image sensor different from the first image sensor, and a display, and includes: one or more processors and / or circuitry configured to execute a first acquisition processing to acquire a first image of an outside world captured by the first image sensor; execute a second acquisition processing to acquire a second image of the outside world captured by the second image sensor; and execute a generation processing to generate, on a basis of the distance information corresponding to the first image, an image of the outside world, which is a color image to be displayed on the display, by combining the first image which is a grayscale image, and the second image which is a color image.

Multiple successively spatially downsampled versions of a rendered image frame are generated for at least one two-dimensional signal component of one or more two-dimensional signal components of an image frame, and one or more versions of the rendered image frame are selected from among the rendered image frame and the spatially downsampled versions of the rendered image frame for sampling a texture feature based, at least in part, on a prediction computed by a neural network.

Techniques for generating modified images using content information and style information are disclosed. First image data comprising image content information is received, and a content encoder generates a first embedding by extracting the image content information from the first image data. A second embedding generated by a style encoder is received, the second embedding comprising style information of second image data. The style information comprises color information and texture information. A decoder generates a modified image using the first embedding and the second embedding, the modified image comprising the image content information of the first image data and the style information of the second image data.

According to an embodiment, a wearable device may display, based on contact on a second surface identified using a touch sensor, a visual object indicating a first position of the touch input on the second surface in a screen through a display. The wearable device may identify, in response to the touch input, a second position in the screen of a gaze identified based on an image obtained through a camera exposed outside at a portion of a first surface. The wearable device may provide, in response to the second position identified within a specified distance from the first position of the visual object, feedback with respect to the touch input. The wearable device may cease to provide the feedback in response to the second position identified outside from the first position by the specified distance.

A method for evaluating and optimizing user experience in human-computer interaction includes: acquiring a multimodal biological signal of a user during human-computer interaction, and preprocessing the multimodal biological signal to extract a fixation position and index parameters of multiple dimensions; standardizing the index parameters, and performing, based on each index parameter and a weight for each index parameter, weighted average to calculate a user experience evaluation score of each dimension; acquiring a weight for the user experience evaluation score, and performing weighted average to calculate an overall user experience evaluation result; visually presenting, based on the overall user experience evaluation result, user experience situation data of the user for a current fixation position; and acquiring a correction parameter of the user, and optimizing, based on the correction parameter, the weights using a machine learning optimization algorithm.

An image sensor for color imaging using arbitrary-color-filter-array event data is provided. The image sensor comprises: a plurality of color imaging pixels and a plurality of color event pixels. An image signal of a first color imaging pixel included in the plurality of color imaging pixels is determined based on a first color signal of the first color image pixel and at least a color event data of one or more color event pixels included in the plurality of color event pixels. The color event data is generated in a temporal relation to the generation of the first color signal.