15677 results about "Image generation" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

The invention relates to a microchip appearance defect detection method based on a convolutional neural network. The microchip appearance defect detection method comprises the following steps: acquiring a high-resolution microscopic image and carrying out adaptive brightness equalization processing; labeling defect features in the pre-processed image by adopting a layered labeling strategy, and dividing defects into a surface deformation class and a material anomaly class by coarse-grained classification labeling; a double-branch lightweight convolutional neural network model is constructed based on the annotation data, a first branch extracts global semantic features, and a second branch extracts local detail features. In the model training, a defect sensitivity loss function is adopted for optimization, and defect area attention, edge preservation and category balance loss items are included, so that the small-size defect detection capability and the boundary positioning precision are improved, and the problem of sample imbalance is solved. And finally, a detection result including defect types, position coordinates and confidence scores is generated by inputting a to-be-detected preprocessed microscopic image, and the defect detection performance with high precision and strong pertinence is achieved.

The method can include: recording a neural state, determining a latent array, and determining an output based on the latent array. In variants, the method can function to enable a subject to use neural signals to navigate within a high-dimensional concept space to control an output (e.g., image generation, motor control, communication, etc.).

The invention discloses an intelligent multi-modal virtual digital human interaction system based on an AI language large model. The system comprises a high-authenticity face generation module; the high-authenticity face generation module uses an AdaAN network, based on adaptive feature fusion and voice driving and time sequence modeling of voice features, feature information related to voice is extracted, the extracted voice features are processed through a deep neural network, it is ensured that the voice and facial expressions are highly aligned in time and space, and the face recognition accuracy is improved. Collecting a bio-electricity signal, mapping the signal to facial muscle movement, generating a final facial expression, and interacting with a user; the system further comprises an intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interaction scene adaptation module and a feedback optimization module. The invention further discloses a multi-mode digital human interaction method which has wide application value.

The invention provides a scene reconstruction method based on delayed rendering and three-dimensional Gaussian. The method comprises the following steps: S1, generating initial three-dimensional point cloud data based on a multi-view image; s2, constructing a trainable structural body for three-dimensional Gaussian modeling; s3, normal initialization and residual optimization are carried out on the Gaussian ellipsoid primitives, depth consistency constraint is combined, and a differentiable and learnable normal reconstruction mechanism is realized, so that the geometric expression ability of illumination modeling is enhanced; s4, introducing a reflection training mechanism based on ambient light and a reflection direction, and generating a Gaussian attribute based on a visual angle; and S5, a final image is generated through a differentiable Gaussian sputtering rendering algorithm, and optimization is carried out through pixel loss of the final image and a real image. According to the method, the reality sense and geometric consistency of the Gaussian sputtering model under the complex illumination condition are remarkably improved, and the technical problems of unreal rendering effect, inaccurate surface normal estimation, weak propagation capability and the like of the existing three-dimensional Gaussian sputtering model under the complex illumination condition are solved.

An illustrative method may include receiving images of a site captured at a same time by a camera, the images comprising a single visible color component image, a first non-visible image associated with first excitation illumination having a first wavelength outside a visible spectrum, and a second non-visible image associated with second excitation illumination having a second wavelength outside the visible spectrum and different than the first wavelength; generating, based on the single visible color component image, a monochromatic image; generating, based on the single visible color component image and the first non-visible image, a first alternate image representative of a first alternate imaging characteristic of the site; generating, based on the single visible color component image and the second non-visible image, a second alternate image representative of a second alternate imaging characteristic of the site; and displaying the monochromatic image combined with the first and second alternate images.

The method can include: recording a neural state, determining a latent array, and determining an output based on the latent array. In variants, the method can function to enable a subject to use neural signals to navigate within a high-dimensional concept space to control an output (e.g., image generation, motor control, communication, etc.).

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enabling artificial intelligence to generate new images based on contextual data and to generate digital components based on the images. In one aspect, a method includes receiving one or more queries from a client device of a user. A digital component is selected based on the one or more queries. A customized digital component is generated by obtaining an image of an object corresponding to the selected digital component and generating, using a language model, an image editing prompt for editing the image based on digital component data related to the digital component and query data including the one or more queries and contextual data. The image and the image editing prompt are provided to an image editing model. An edited image is received and used to generate the customized digital component.

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.

A method includes receiving a pointer to a plurality of images of a web page, the web page configured to display the plurality of images in a first arrangement; retrieving, using the pointer, the plurality of images of the web page; executing at least one machine learning model using the plurality of images from the web page as input to generate at least one image performance score for each image of the plurality of images, the at least one machine learning model trained based on a training set of images labeled based at least on interaction data corresponding to images of the training set of images; and rearranging the plurality of images on the web page to a second arrangement according to the at least one image performance score generated for each image of the plurality of images of the web page.

Systems and methods are described for inputting text input to a trained machine learning model; generating, using the trained machine learning model and based on the text input, a single-layer image comprising a plurality of objects; segmenting the single-layer image to generate a plurality of images, each image of the plurality of images comprising a depiction of a respective object of the plurality of objects of the single-layer image; extracting, from the text input, a portion of the text input describing a background portion of the single-layer image; generating, using the trained machine learning model and based on the extracted portion of the text input, a background image; and generating the multi-layer image based on the plurality of images and the background image.

The present application provides an image generation method and apparatus based on artificial intelligence, an electronic device, a computer readable storage medium, and a computer program product. The method comprises: acquiring a content text and acquiring a style image having a target style; carrying out text encoding processing on the content text to obtain a content text code of the content text, and extracting a style code from the style image; and on the basis of a dual-path cross attention mechanism corresponding to the style code and the content text code, carrying out inverse diffusion processing on a noise image to obtain a target image, wherein the target image is matched with the content of the content text, and the target image has the target style.

A method, apparatus, and non-transitory computer readable medium for image generation are described. Embodiments of the present disclosure obtain a content input and a style input via a user interface or from a database. The content input includes a target spatial layout and the style input includes a target style. A content encoder of an image processing apparatus encodes the content input to obtain a spatial layout mask representing the target spatial layout. A style encoder of the image processing apparatus encodes the style input to obtain a style embedding representing the target style. An image generation model of the image processing apparatus generates an image based on the spatial layout mask and the style embedding, where the image includes the target spatial layout and the target style.

The invention discloses a multi-modal semantic and physical law driven remote sensing image generation method, belongs to the technical field of computer vision and remote sensing image generation, and aims to solve the problems of insufficient cross-modal semantic alignment, low reliability of a generation result and insufficient physical mechanism fusion. The four-stage method comprises the following steps: firstly, rejecting low-quality samples from original data and unifying a spatial scale; then, extracting a multi-modal semantic vector by adopting a BLIP model and a CLIP model, and introducing a remote sensing physical rule to carry out vector optimization; then position coding and physical constraint conditions are embedded in the submerged space, and multi-source information joint modeling is achieved through a cross-modal encoder; and finally, by taking text description, physical priori knowledge and diffusion time steps as joint conditions, performing de-noising reasoning based on a Transform architecture, and completing back diffusion reconstruction by means of a trans-attention mechanism. According to the method, physical rationality and semantic consistency are improved, and a more reliable technical normal form is provided for remote sensing image generation in the fields of disaster monitoring, military simulation and the like.

The invention discloses a construction progress dynamic optimization method and system based on BIM and computer vision, and particularly relates to the technical field of building construction management, and the method comprises the steps: carrying out the automatic registration of a BIM model and a construction site image; processing the construction site image by adopting a visual identification algorithm to generate a visual identification result; constructing a four-dimensional dynamic BIM model, and mapping a visual identification result to a corresponding component in real time through multi-feature similarity calculation; the progress deviation is monitored by using key path dynamic identification and a deviation propagation matrix, and the risk is predicted by combining a Bayesian network and Monte Carlo simulation. The BIM and computer vision technologies are fused, a construction progress optimization system integrating automatic registration, dynamic monitoring, risk prediction and intelligent decision making is constructed, and the problems that traditional manual inspection data collection is low in efficiency, progress monitoring is lagged, risk prejudgment is fuzzy and resource allocation is extensive are solved; accurate monitoring, risk early warning and resource optimization configuration of the construction progress are realized.

Methods and systems are described for generating memes and enhanced content in electronic communication. The communication is received, analyzed, and assessed for determining whether the enhanced content should be included in the communication. A content, context, intent, and / or mood of the communication is determined. For meme generation implementations, images and captions are generated based on the communication. Some candidate memes incorporate an image of one of the participants in the communication. Enhanced content candidates are generated and presented to the user for inclusion in the communication. Artificial intelligence systems, including neural networks, and models are utilized to improve the enhanced content generation. Related apparatuses, devices, techniques, and articles are also described.

An image generation system is described. The system comprises a neural network model configured to perform a diffusion process to generate a set of multi-view images from a same input prompt. The set of multi-view images have a same subject from different view orientation. The neural network model comprises a self-attention layer configured to relate pixels across the set of multi-view images.

Systems and methods are described for generating, using the first trained machine learning model and based on text input, a single-layer image comprising a plurality of objects; generating a plurality of masks associated with the plurality of objects; determining a plurality of attributes associated with the plurality of objects; generating, using a second trained machine learning model, a plurality of textual descriptions respectively corresponding to the plurality of objects; inputting the plurality of textual descriptions, and the plurality of attributes, to the first trained machine learning model; generating, using the first trained machine learning model, a plurality of images respectively corresponding to the plurality of textual descriptions; and generating the multi-layer image by combining the plurality of images and by using the plurality of masks, wherein the plurality of images respectively correspond to a plurality of layers of the multi-layer image.

The invention discloses an emotion-enhanced digital human driving and presenting system and method, and belongs to the technical field of computers.The system comprises an audio decoupling module, a potential diffusion model module, a human motion prior module, a training module and a rendered image generation module; predicting and reconstructing the facial expression and gesture action sequence to obtain a final action sequence; and generating a neural rendering image based on the final action sequence. According to the method, through voice input, emotion and style features in voice are captured, a cross attention feature fusion mechanism is introduced to enhance expression of emotion and personal style features, a voice-driven gesture generation task is expanded to a facial expression and gesture action synchronous generation task, the integrity of generated digital human actions is realized, and the user experience is improved. The emotional expressive force of facial expressions and gestures of the digital human is greatly improved.

The invention relates to the technical field of industrial component detection, in particular to a transformer iron core detection method based on computer vision, which comprises the following steps of: acquiring an iron core image, extracting key pixel characteristics, screening a directional scattering abnormal region to generate an interference map, extracting a consistent gradient region correction image to generate a reconstruction map, and positioning a symmetric disturbance generation structure map by integral gray difference. And analyzing an overlapping relation by a superposition structure graph to generate an abnormal component graph, and evaluating a risk level by matching a reference index to generate an early warning graph layer. Interference reflection and structural features can be distinguished through linkage analysis of the pixel direction vector and the brightness change frequency, correction of a distorted area in an image is realized based on a gray statistical stable value, and the distortion of the image is corrected by constructing a symmetric point map and analyzing the change trend of a gradient difference value sequence. And the structural overlapping relation is quantitatively judged by combining a component mapping profile diagram, so that the relevance between an abnormal region and a key component is clearly expressed, and the grading evaluation capability of various fault risks in the iron core is improved.

An apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy including an overlay. The apparatus includes at least a processor configured receive a set of images of a cardiac anatomy pertaining to a subject, generate a set of shape parameters based on the set of images, wherein generating the set of shape parameters includes receiving cardiac geometry training data including a plurality of image sets as input correlated to a plurality of shape parameter sets as output, training a shape identification model using the cardiac geometry training data, and generating the set of shape parameters using the shape identification model, generate a 3D model of the cardiac anatomy based on the set of shape parameters, generate a map by determine a level of uncertainty at each location of a plurality of locations on the generated 3D model, and overlay the map onto the 3D model.

Disclosed herein are methods, systems, and computer-readable media for regenerating a region of an image with a machine learning model based on a text input. Disclosed embodiments involve accessing a digital input image. Disclosed embodiments involve generating a masked image by removing a masked region from the input image. Disclosed embodiments involve accessing a text input corresponding to an image enhancement prompt. Disclosed embodiments include providing at least one of the input image, the masked region, or the text input to a machine learning model configured to generate an enhanced image. Disclosed embodiments involve generating, with the machine learning model, the enhanced image based on at least one of the input image, the masked region, or the text input.

The invention discloses a remote sensing image generation method based on a federal visual language model, which belongs to the technical field of machine learning and specifically comprises the following steps: receiving text instruction description by each client; extracting a multi-scale feature map from private remote sensing image data through a visual encoder, and generating a semantic embedding vector by text instruction description through a language encoder; inputting the semantic embedding vector and the multi-scale feature map into a dynamic attention mask generator to generate pixel-level space weight distribution; carrying out weighted fusion operation on the multi-scale feature map, and generating visual feature representation of text conditionalization; generating a remote sensing image according with the description of the text instruction through an image decoder; the client uploads model parameter increments of the visual encoder, the language encoder and the dynamic attention mask generator to the central server; the central server aggregates the model parameter increments, and distributes the updated global model parameters to each client; according to the method, the flexibility and semantic consistency of remote sensing image generation are effectively improved.

The invention belongs to the technical field of image generation, and particularly discloses a multi-modal scene fusion method based on a large diffusion model. Firstly, singular value decomposition is carried out on a weight matrix of a diffusion model backbone network to update the weight matrix of the backbone network; then, inputting the text cue word into the diffusion model to generate a text condition image; performing VAE decoding and preprocessing on the text condition image to obtain a preprocessed text condition image; inputting the preprocessed text condition image into a pre-trained visual classifier to predict the category, and obtaining a prediction label; updating loss is calculated according to the predicted label and the real label, parameters of the diffusion model are updated, and fine adjustment of the diffusion model is achieved; and finally, generating a scene fusion image by using the fine-tuned diffusion model. Scene fusion is divided into two stages, in the first stage, a combined noise image is generated by sampling a background image and a foreground image, and in the second stage, a scene fusion image is generated through iteration of the combined noise image. According to the method, image generation is guided through the image, seamless fusion of the foreground image and the background image is achieved, meanwhile, image generation is guided through control information, and controllability of scene fusion is enhanced.

The invention discloses an infrared and visible light image fusion method based on a conditional diffusion model, and belongs to the technical field of image fusion. Compared with a current popular infrared and visible light image fusion algorithm, the algorithm is obviously improved in the aspects of color fidelity, texture detail maintenance and fusion quality, and feature extraction and fusion tasks are completed through combination of a conditional denoising diffusion implicit model and a multi-channel high and low frequency parallel fusion model; the problem that the denoising diffusion implicit model lacks a reference true value of a fusion result when executing an image fusion task is relieved, the unique advantages of the denoising diffusion implicit model in image processing are further shown, and the method is excellent in the aspects of high-quality image generation and mathematical interpretability. The optimization of the existing method and the upgrading of the existing technology are realized.

Systems and methods for generating a 3D model from a single input image are described. Embodiments are configured to obtain an input image and camera view information corresponding to the input image; encode the input image to obtain 2D features comprising a plurality of 2D tokens corresponding to patches of the input image; decode the 2D features based on the camera view information to obtain 3D features comprising a plurality of 3D tokens corresponding to regions of a 3D representation; and generate a 3D model of the input image based on the 3D features.

A data processing system implements receiving, via a user interface of a client device, images for generating a collage image; generating captions for the images; constructing a first prompt by appending the captions to a first instruction string including instructions to a generative language model to extract a theme from the captions; providing the first prompt to the generative language model and receiving the theme therefrom; constructing a second prompt by appending the theme to a second instruction string including instructions to a text-to-image model to use the theme to create a background image with placeholders; providing the second prompt to the text-to-image model and receiving the background image therefrom; identifying the placeholders in the background image; creating the collage image by fitting the images into the identified placeholders; providing the collage image to the client device; and causing the user interface to display the collage image.

Systems and methods for generating images using hybrid sampling include obtaining a noisy image and generating a first denoised image during a first reverse diffusion phase using a diffusion neural network. The first denoised image is generated based on a first sampler that uses a first sampling density during at least a portion of the first reverse diffusion phase. Subsequently, a second denoised image is generated based on the first denoised image during a second reverse diffusion phase using the diffusion neural network. The second denoised image is generated based on a second sampler that uses a second sampling density different from the first sampling density during at least a portion of the second reverse diffusion phase.

A method, apparatus, non-transitory computer readable medium, apparatus, and system for image generation include obtaining a sketch input depicting an object, processing the sketch input to obtain sketch guidance, and generating a synthesized image based on the sketch guidance using an image generation model, where the synthesized image depicts the object from the sketch input.

The invention discloses an editable condition printing image generation method, and belongs to the field of image generation. The method comprises the following steps: constructing a printing image generation model, and training the model to obtain a generation model with editable conditions; and generating a printing design image meeting the condition requirements by using the model. According to the method, different advanced features of each element of the printed plane image are extracted through downsampling, element and style information in the printed plane image is captured through multi-scale feature extraction and information exchange, then hidden space vectors corresponding to different printed plane image features are analyzed, fusion of specific styles is achieved in the hidden space, and the image quality is improved. And the decoding of the printing design image is realized through the decoder, the decoupling of the element information is realized, the personalized design and diversified requirements can be met, and the expansibility is realized.