Abstract: The specification discloses image and data processing methods and apparatuses. The method includes: obtaining a source pose and texture information according to a source image; obtaining a first synthetic image according to the source image, a target pose, and the source pose; obtaining a residual map according to the texture information and the first synthetic image; and obtaining a second synthetic image according to the first synthetic image and the residual map. The specification resolves the technical problem of lacking a sense of reality in a synthetic image due to loss of texture details in feature extraction during character action transfer in the existing technologies.

Abstract: A method for generating a synopsis video includes acquiring a target video and parameter data related to editing of the target video, wherein the parameter data comprises at least a duration parameter of a synopsis video of the target video; extracting a plurality of pieces of image data from the target video, and determining an image label of the image data, wherein the image label comprises at least a visual-type label; and determining a type of the target video, and establishing a target editing model for the target video according to the type of the target video, the duration parameter, and a plurality of preset editing technique submodels; and editing the target video according to the image label of the image data in the target video by using the target editing model to obtain the synopsis video of the target video

Abstract: The specification discloses image and data processing methods and apparatuses. The method includes: obtaining a source pose and texture information according to a source image; obtaining a first synthetic image according to the source image, a target pose, and the source pose; obtaining a residual map according to the texture information and the first synthetic image; and obtaining a second synthetic image according to the first synthetic image and the residual map. The specification resolves the technical problem of lacking a sense of reality in a synthetic image due to loss of texture details in feature extraction during character action transfer in the existing technologies.

Abstract: The present application discloses a method, device, and system for processing a medical image. The method includes obtaining a first image corresponding to a to-be-processed image, obtaining a second image corresponding to the to-be-processed image, wherein the first image and the second image are obtained in response to an image degradation processing being performed with respect to the to-be-processed image, and an image quality of the first image and an image quality of the second image are different. The method further includes obtaining image structural information corresponding to the to-be-processed image, and performing an image fusion processing with respect to the first image and the second image to image fusion based at least in part on the image structural information corresponding to the to-be-processed image, wherein a target image is obtained based at least in part on the image fusion processing, and the target image corresponds to the to-be-processed image.

Abstract: The specification discloses image and data processing methods and apparatuses. The method includes: obtaining a source pose and texture information according to a source image; obtaining a first synthetic image according to the source image, a target pose, and the source pose; obtaining a residual map according to the texture information and the first synthetic image; and obtaining a second synthetic image according to the first synthetic image and the residual map. The specification resolves the technical problem of lacking a sense of reality in a synthetic image due to loss of texture details in feature extraction during character action transfer in the existing technologies.

Abstract: In a video frame processing system, a feature extractor generates, based on a plurality of data sets corresponding to a plurality of frames of a video, a plurality of feature sets, respective ones of the feature sets including features extracted from respective ones of the data sets. A first stage of the feature aggregator generates a kernel for a second stage of the feature aggregator. The kernel is adapted to content of the feature sets so as to emphasize desirable ones of the feature sets and deemphasize undesirable ones of the feature sets. In the second stage of the feature aggregator the kernel generated by the first stage is applied to the plurality of feature sets to generate a plurality of significances corresponding to the plurality of feature sets. The feature sets are weighted based on corresponding significances and weighted feature sets are aggregated to generate an aggregated feature set.

Abstract: In a video frame processing system, a feature extractor generates, based on a plurality of data sets corresponding to a plurality of frames of a video, a plurality of feature sets, respective ones of the feature sets including features extracted from respective ones of the data sets. A first stage of the feature aggregator generates a kernel for a second stage of the feature aggregator. The kernel is adapted to content of the feature sets so as to emphasize desirable ones of the feature sets and deemphasize undesirable ones of the feature sets. In the second stage of the feature aggregator the kernel generated by the first stage is applied to the plurality of feature sets to generate a plurality of significances corresponding to the plurality of feature sets. The feature sets are weighted based on corresponding significances and weighted feature sets are aggregated to generate an aggregated feature set.

Abstract: Some implementations disclosed herein provide techniques and arrangements to render global light transport in real-time or near real-time. For example, in a pre-computation stage, a first computing device may render points of surfaces (e.g., using multiple light bounces and the like). Attributes for each of the points may be determined. A plurality of machine learning algorithms may be trained using particular attributes from the attributes. For example, a first machine learning algorithm may be trained using a first portion of the attributes and a second machine learning algorithm may be trained using a second portion of the attributes. The trained machine learning algorithms may be used by a second computing device to render components (e.g., diffuse and specular components) of indirect shading in real-time.

Abstract: Some implementations disclosed herein provide techniques and arrangements to render global light transport in real-time or near real-time. For example, in a pre-computation stage, a first computing device may render points of surfaces (e.g., using multiple light bounces and the like). Attributes for each of the points may be determined. A plurality of machine learning algorithms may be trained using particular attributes from the attributes. For example, a first machine learning algorithm may be trained using a first portion of the attributes and a second machine learning algorithm may be trained using a second portion of the attributes. The trained machine learning algorithms may be used by a second computing device to render components (e.g., diffuse and specular components) of indirect shading in real-time.

Abstract: Some implementations disclosed herein provide techniques and arrangements to render global light transport in real-time or near real-time. For example, in a pre-computation stage, a first computing device may render points of surfaces (e.g., using multiple light bounces and the like). Attributes for each of the points may be determined. A plurality of machine learning algorithms may be trained using particular attributes from the attributes. For example, a first machine learning algorithm may be trained using a first portion of the attributes and a second machine learning algorithm may be trained using a second portion of the attributes. The trained machine learning algorithms may be used by a second computing device to render components (e.g., diffuse and specular components) of indirect shading in real-time.

Abstract: A system for reflectance acquisition of a target includes a light source, an image capture device, and a reflectance reference chart. The reflectance reference chart is fixed relative to the target. The light source provides a uniform band of light across at least a dimension of the target. The image capture device is configured and positioned to encompass at least a portion of the target and at least a portion of the reflectance reference chart within a field-of-view of the image capture device. The image capture device captures a sequence of images of the target and the reflectance reference chart during a scan thereof. Reflectance responses are calculated for the pixels in the sequence of images. Reference reflectance response distribution functions are matched to the calculated reflectance responses, and an image of the target is reconstructed based at least in part on the matched reference reflectance response distribution functions.

Abstract: Some implementations disclosed herein provide techniques and arrangements to render global light transport in real-time or near real-time. For example, in a pre-computation stage, a first computing device may render points of surfaces (e.g., using multiple light bounces and the like). Attributes for each of the points may be determined. A plurality of machine learning algorithms may be trained using particular attributes from the attributes. For example, a first machine learning algorithm may be trained using a first portion of the attributes and a second machine learning algorithm may be trained using a second portion of the attributes. The trained machine learning algorithms may be used by a second computing device to render components (e.g., diffuse and specular components) of indirect shading in real-time.

Abstract: A system for reflectance acquisition of a target includes a light source, an image capture device, and a reflectance reference chart. The reflectance reference chart is fixed relative to the target. The light source provides a uniform band of light across at least a dimension of the target. The image capture device is configured and positioned to encompass at least a portion of the target and at least a portion of the reflectance reference chart within a field-of-view of the image capture device. The image capture device captures a sequence of images of the target and the reflectance reference chart during a scan thereof. Reflectance responses are calculated for the pixels in the sequence of images. Reference reflectance response distribution functions are matched to the calculated reflectance responses, and an image of the target is reconstructed based at least in part on the matched reference reflectance response distribution functions.

Abstract: Disclosed is a system and method for determining, in parallel on a graphics processing unit, a block compression case which results in a least error to a block. Once determined, the block compression case may be used to compress the block.