716results 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.

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.

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.

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.

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.

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.

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.

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.

An ASDGS (Artificially Spiked Data Generation System) may comprise computing and mechanical systems for using a single class of data to generate artificially “spiked” data of a second class. To generate each spiked image, the ASDGS may randomly select a clean image an augmentation object (“AO”) from an object library, shape library, and / or hair library. The ASDGS may use a texture library to add or change the texture of the AO. The ASDGS may adjust the lighting and coloring of the AO to be similar to the clean image, and may then add the AO to the clean image to generate a spiked image.

Disclosed systems and methods convert user-supplied textual content into animated videos. Textual input is received and processed to identify narrative elements such as characters, settings, and events. These elements are then transformed into visual scene components. Still images generated based on these components are subsequently animated in line with the narrative context. The system can automatically implement storytelling techniques adapted to incorporate neuroscience principles. The system also synthesizes speech for dialogues or narrations using voice synthesis technology that considers emotional markers, tone, and pace. Generated media and metadata are stored in a data storage system that maintains data integrity and enables efficient retrieval. Users can interact with an export interface to choose video resolution, format, and sharing options. A feedback system employing machine learning algorithms collects and analyzes user feedback for real-time adjustments to the generated animated video.

Systems and methods for performing integrity verification of content in a video conference using lighting adjustment are provided. An example method includes determining that an integrity verification of video content generated by a first client device of a plurality of client devices of a plurality of participants of a video conference is to be performed; causing a modified UI comprising one or more visual items, each corresponding to a video stream, to be presented on the first client device, wherein the UI was modified using a color pattern encoding; receiving, from the first client device, a video stream generated by the first client device subsequent to a presentation of the modified UI on the first client device; and verifying the integrity of the video content generated by the first client device based on the video stream generated by the first client device and the color pattern encoding.

Examples provide an automatic item display reconfiguration guidance system providing sequential reconfiguration instructions presented to a user coordinated across multiple display devices in real-time as the modular item display is being reconfigured from an original item configuration to a new item configuration. A sequence of graphical instructions for removing a set of items, moving a set of items, and adding a set of new items to the modular item display are generated and presented to a user via a user interface (UI) on a user device and / or one or more shelf display device(s) on the modular item display in a substantially simultaneous manner. The graphical instructions including, color-coded indicators and / or images of the items, are presented to the user in real-time as items are removed, moved, or added in sequence enabling fast and efficient reconfiguration of items on the display with minimized expenditure of labor and reduced error rate.

Systems and methods train an encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The encoder is trained by providing an input training image to the encoder and producing, by the encoder, a latent space representation of the input training image. The latent space representation is provided as input to the GAN to generate a generated training image. A latent code is sampled from a latent space associated with the GAN and the sampled latent code is provided as input to the GAN. The GAN generates a synthetic training image based on the sampled latent code. The sampled latent code is provided as input to the encoder to produce a synthetic training code. The encoder is updated by minimizing a loss between the generated training image and the input training image, and the synthetic training code and the sampled latent code.

A hair straightening brush with a thin film transistor-liquid crystal display (TFT-LCD) includes a head part and a handheld part. The head part includes a first housing, metal heat-conducting teeth, and an electric heating component. The handheld part includes a second housing, and a main control unit. The electric heating component is electrically connected to the main control unit. A display module is further disposed in the second housing, is provided with a display (TFT-LCD), and is electrically connected to the display. The display module includes a driver circuit and a signal control module, and is configured to generate timing signals for controlling row and column drivers of the display. The driver circuit is configured to: receive the timing signals for controlling the row and column drivers, convert the timing signals into row and column driver analog voltages, output the analog voltages to the display, and control liquid crystal alignment of the display.

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilizes a segmentation approach that distinguishes between smooth-shaded regions from high-frequency regions in an image within a vectorization pipeline to generate a vector image. For instance, the disclosed systems utilize a smoothing function to identify non-overlapping sets of pixels that include locally smooth pixels and pixels with high frequency details for an image. Furthermore, in some instances, the disclosed systems generate separate sets of fill functions (representing color-based regions) using color-based pixel clustering for the non-overlapping sets of pixels. Moreover, in one or more instances, the disclosed systems merge neighboring color-based regions in the sets of fill functions (using color similarity) to generate a set of segmented regions for an image. In some implementations, the disclosed systems utilize the set of segmented regions, from the image, to generate a vector image from the image.

An audio output device case includes a display device. While the display device is disabled, the display device and an exterior of the audio output device case meet similarity criteria. The audio output device case detects an occurrence of a first event. In response to detecting the occurrence of the first event and in accordance with a determination that the display device is disabled, the display device is enabled. A dynamic visual element corresponding to the first event is displayed via a first portion of the display device. The dynamic visual element changes over time. While the display device is enabled, a second portion of the display device, distinct from the first portion of the display device, and the exterior of the audio output device case meet the similarity criteria.

HDR color patches are sampled throughout an HDR color space parameterized by a parameter. Reference SDR color patches, input HDR color patches and reference HDR color patches are generated from the sampled HDR color patches. An optimization algorithm is executed to generate an optimized forward reshaping mapping and an optimized backward reshaping mapping. The optimized forward reshaping mapping is used to forward reshape input HDR images into forward reshaped SDR images, whereas the optimized backward reshaping mapping is used to backward reshape the forward reshaped SDR images into backward reshaped HDR images.

A system and method are provided for generating colorized image data for a single-channel image using a ML network, as well as a system and method for training the ML network. The image colorization method includes: obtaining single-channel image data representing a single-channel image; generating image feature data as a result of inputting the single-channel image data into an encoder; generating a pixel decoder output through inputting the image feature data into a pixel decoder; generating a color decoder output through inputting the image feature data into a color decoder; and generating colorized image data based on the color decoder output and the pixel decoder output, wherein the colorized image data represents a colorized version of the single-channel image.

This application relates to a face image processing method, apparatus, computer device, and storage medium. The method includes acquiring a first face image and a second face image, the first face image and the second face image being images of real faces; generating a first updated face image with non-real face image characteristics based on the first face image; adjusting color distribution of the first updated face image according to color distribution of the second face image to obtain a first adjusted face image; acquiring a target face mask of the first face image, the target face mask being generated by randomly deforming a face region of the first face image; and blending the first adjusted face image and the second face image according to the target face mask to obtain a target face image. Accordingly, a diversity of target face images can be generated.

In some implementations, a computing device in communication with an immersive content generation system may generate a first set of user interface elements configured to receive a first selection of a shape of a virtual stage light. In addition, the device may generate a second set of user interface elements configured to receive a second selection of an image for the virtual stage light. Also, the device may generate a third set of user interface elements configured to receive a third selection of a position and an orientation of the virtual stage light. Further, the generate a fourth set of user interface elements configured to receive a fourth selection of a color for the virtual stage light. Numerous other aspects are described.

A technique for performing style transfer between a content sample and a style sample is disclosed. The technique includes applying one or more neural network layers to a first latent representation of the style sample to generate one or more convolutional kernels. The technique also includes generating convolutional output by convolving a second latent representation of the content sample with the one or more convolutional kernels. The technique further includes applying one or more decoder layers to the convolutional output to produce a style transfer result that comprises one or more content-based attributes of the content sample and one or more style-based attributes of the style sample.

Embodiments of the disclosure provide a method, apparatus, electronic device and storage medium for processing image, and the method includes: obtaining an image to be processed; determining an object structural feature within the image to be processed corresponding to a target object and determining a style texture feature corresponding to a reference style image to be applied; and determining a target style image corresponding to the image to be processed based on the object structural feature and the style texture feature.

A method and system for reducing the chromatic aberration in an image, more particularly using a data processing apparatus or system for reducing the chromatic aberration in an image of an environment that has been captured by an image capturing sensor and which is supposed to be displayed to a user. In particular, this method can be used on an image that has been captured by a wide-angle camera with the aim of displaying it to the driver of a vehicle (passenger car, transport truck, motorcycle, etc.) in order for instance to replace the internal and external mirrors of the vehicle.

Disclosed is a method, a device, and / or a system of rapid rendering and / or realistic visualization of apparel design draft files through application of one or more generative artificial neural networks. In one embodiment, a system includes a coordination server, a generative server, and a network. A draft receipt agent receives a draft file including a sketch of an apparel item. The description module receives a text description of attribute(s) of the apparel item. A generative model selection routine selects a generative image model configured to generate an output image constrained by the draft file and latent representations of a text-image relation model. The model parameterization subroutine selects the text-image relation model and parameterizes the generative image model. A generative model execution engine generates a first rendering file including modified by the text description to allow for rapid visualization, prototyping, and / or construction of the one or more apparel items.

Embodiments of the present disclosure provide techniques for colorizing visual content using artificial intelligence models. An example method generally includes receiving an image and an input prompt specifying a colorization to apply to the image. Based on an encoded version of the image and a textual description of the image input into a machine learning model, one or more color maps associated with the specified colorization to apply to the image are generated. A colorized version of the image is generated by a generative artificial intelligence model based on combining a grayscale version of the image and the one or more color maps, and the colorized version of the image is output.

Disclosed is a method of generating a relighted image based on an object image according to various embodiments of the present invention for realizing the problems described above. The method includes acquiring a source original image, acquiring image characteristic information based on the source original image, and generating the relighted image based on the source original image, the image characteristic information, and target lighting information, in which the relighted image is an image reflecting a realistic human skin tone, texture, and a shadow effect under the target lighting conditions, and is an image whose a lighting effect is changed compared to the source original image.

A model determining method including obtaining a first image sample, inputting the first image sample to an initial encoder of an initial identification model to obtain a first image sample feature, generating a second image sample feature based on the first image sample feature, separately inputting the first and second image sample features to an initial decoder to obtain a first texture image and a second texture image, inputting the first image sample feature and the first texture image to an initial classifier to obtain a first prediction result, inputting the second image sample feature and the second texture image to the initial classifier to obtain a second prediction result, generating an identification loss function based on a difference between each of the first and the second prediction results and an identification tag, and training an initial identification model using the identification loss function to obtain an updated identification model.