Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, in one or more embodiments, the disclosed systems receive an input digital image and search input and further modify the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and the disclosed systems retrieve the image search results utilizing a weighted combination of the queries. In some implementations, the disclosed systems generate an input embedding for the search input (e.g., the multi-modal search input) and retrieve the image search results using the input embedding.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.

Abstract: Systems, methods, and non-transitory computer-readable media implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, one or more embodiments involve receiving an input digital image and search input and further modifying the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and one or more embodiments involve retrieving the image search results utilizing a weighted combination of the queries. Some implementations involve generating an input embedding for the search input (e.g., the multi-modal search input) and retrieving the image search results using the input embedding.

Abstract: An audio signal playing method and apparatus, and an electronic device are provided. The method comprises: separating, from a first audio signal, a recorded audio signal corresponding to each of at least one sound source; on the basis of the first audio signal, determining a real-time orientation of each of the at least one sound source relative to the head of a user; for each sound source, according to the real-time orientation of the sound source and the recorded audio signal corresponding to the sound source, generating a target direct audio signal corresponding to the sound source, and generating a target reverberated audio signal corresponding to the sound source; and playing a second audio signal that is generated by means of fusing the target direct audio signal and the target reverberated audio signal corresponding to each sound source.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that trains (and utilizes) an image color editing diffusion neural network to generate a color edited digital image(s) for a digital image. In particular, in one or more implementations, the disclosed systems identify a digital image depicting content in a first color style. Moreover, the disclosed systems generate, from the digital image utilizing an image color editing diffusion neural network, a color-edited digital image depicting the content in a second color style different from the first color style. Further, the disclosed systems provide, for display within a graphical user interface, the color-edited digital image.

Abstract: Systems and methods for image processing are configured. Embodiments of the present disclosure encode a content image and a style image using a machine learning model to obtain content features and style features, wherein the content image includes a first object having a first appearance attribute and the style image includes a second object having a second appearance attribute; align the content features and the style features to obtain a sparse correspondence map that indicates a correspondence between a sparse set of pixels of the content image and corresponding pixels of the style image; and generate a hybrid image based on the sparse correspondence map, wherein the hybrid image depicts the first object having the second appearance attribute.

Abstract: Systems and methods for image compositing are provided. An aspect of the systems and methods includes obtaining a first image and a second image, wherein the first image includes a target location and the second image includes a target element; encoding the second image using an image encoder to obtain an image embedding; generating a descriptive embedding based on the image embedding using an adapter network; and generating a composite image based on the descriptive embedding and the first image using an image generation model, wherein the composite image depicts the target element from the second image at the target location of the first image.

Abstract: Embodiments include systems and methods for generative image filling based on text and a reference image. In one aspect, the system obtains an input image, a reference image, and a text prompt. Then, the system encodes the reference image to obtain an image embedding and encodes the text prompt to obtain a text embedding. Subsequently, a composite image is generated based on the input image, the image embedding, and the text embedding.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.

Abstract: An image processing system obtains an input image (e.g., a user provided image, etc.) and a mask indicating an edit region of the image. A user selects an image editing mode for an image generation network from a plurality of image editing modes. The image generation network generates an output image using the input image, the mask, and the image editing mode.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure obtain a noise image and guidance information for generating an image. A diffusion model generates an intermediate noise prediction for the image based on the noise image. A conditioning network generates noise modulation parameters. The intermediate noise prediction and the noise modulation parameters are combined to obtain a modified intermediate noise prediction. The diffusion model generates the image based on the modified intermediate noise prediction, wherein the image depicts a scene based on the guidance information.

Abstract: An audio signal playing method and apparatus, and an electronic device are provided. The method comprises: separating, from a first audio signal, a recorded audio signal corresponding to each of at least one sound source; on the basis of the first audio signal, determining a real-time orientation of each of the at least one sound source relative to the head of a user; for each sound source, according to the real-time orientation of the sound source and the recorded audio signal corresponding to the sound source, generating a target direct audio signal corresponding to the sound source, and generating a target reverberated audio signal corresponding to the sound source; and playing a second audio signal that is generated by means of fusing the target direct audio signal and the target reverberated audio signal corresponding to each sound source.

Abstract: 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.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating scalable and semantically editable font representations utilizing a machine learning approach. For example, the disclosed systems generate a font representation code from a glyph utilizing a particular neural network architecture. For example, the disclosed systems utilize a glyph appearance propagation model and perform an iterative process to generate a font representation code from an initial glyph. Additionally, using a glyph appearance propagation model, the disclosed systems automatically propagate the appearance of the initial glyph from the font representation code to generate additional glyphs corresponding to respective glyph labels. In some embodiments, the disclosed systems propagate edits or other changes in appearance of a glyph to other glyphs within a glyph set (e.g., to match the appearance of the edited glyph).

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via multi-layered scene completion techniques facilitated by artificial intelligence. For instance, in some embodiments, the disclosed systems receive a digital image portraying a first object and a second object against a background, where the first object occludes a portion of the second object. Additionally, the disclosed systems pre-process the digital image to generate a first content fill for the portion of the second object occluded by the first object and a second content fill for a portion of the background occluded by the second object. After pre-processing, the disclosed systems detect one or more user interactions to move or delete the first object from the digital image. The disclosed systems further modify the digital image by moving or deleting the first object and exposing the first content fill for the portion of the second object.

Abstract: A bendable sheath and a delivery system using the bendable sheath. The bendable sheath comprises a tube body (3). The tube body (3) comprises a distal end and a proximal end. A tube wall of the tube body (3) is connected to a pull wire (8). One end of the pull wire (8) extends towards the proximal end of the tube body (3), and the other end is connected to the tube body (3) near the distal end of the tube body (3). The pull wire (8) comprises at least a section thereof disposed freely outside the tube body (3) and near the distal end of the tube body (3). The pull wire (8) in the bendable sheath comprises the section disposed freely outside the sheath tube body (3) and, when pulled, the section is disposed so as to facilitate the application of force. The section moves relative to the tube body (3), such that a force application point is adaptively changed.

Abstract: Provided are an off-grid start method and system for a new energy power generation system. The method includes: gradually boosting the voltage of a master according to a plurality of preset voltage given values, and slaves determining, by means of measuring a voltage of the load of a system, a target voltage given value used by the master; and the master determining, by means of monitoring an output current of the master itself, that a slave is successfully connected in parallel, and then continuing to boost the output voltage until all the slaves run in parallel. Therefore, according to the solution, no upper-layer synchronous control is required during a black-start process, and no communication between a master and slaves is required.

Abstract: A sheath facilitating retraction of prosthetic implant is provided. The sheath includes a tubular body, and the distal end of the body is connected with an expandable section. The expandable section has relative converged configuration and flared configuration and includes a primary expandable area and a secondary expandable area. The primary expandable area includes a plurality of first expandable pieces arranged at intervals in a circumferential direction of the body. The secondary expandable area includes a plurality of second expandable pieces arranged at intervals in the circumferential direction of the body, and all the second expandable pieces include two alternating groups consisting of a first group formed by further extending the first expandable pieces to the distal end and a second group formed by connecting strips wound between two adjacent first expandable pieces.

Abstract: A sound signal processing method, an electronic device, and computer-readable medium are provided. The method includes: importing first frequency spectrum data corresponding to first audio data into a pre-trained sound processing model to obtain a processing result; and generating, based on the processing result, pure audio data corresponding to the first audio data. The sound processing model includes at least one preset convolution layer, and operations performed by using the preset convolution layer includes: performing, based on a first convolution kernel group, a convolution operation on a first sound spectrum feature map inputted into the preset convolution layer, to obtain a second sound spectrum feature map; and combining, based on a second convolution kernel group, the obtained second sound spectrum feature map, to obtain a third sound spectrum feature map corresponding to the second convolution kernel group.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for accurately and flexibly generating scalable fonts utilizing multi-implicit neural font representations. For instance, the disclosed systems combine deep learning with differentiable rasterization to generate a multi-implicit neural font representation of a glyph. For example, the disclosed systems utilize an implicit differentiable font neural network to determine a font style code for an input glyph as well as distance values for locations of the glyph to be rendered based on a glyph label and the font style code. Further, the disclosed systems rasterize the distance values utilizing a differentiable rasterization model and combines the rasterized distance values to generate a permutation-invariant version of the glyph corresponding glyph set.