Abstract: A human activity recognition fusion method and system for ecological protection red line is disclosed. The method includes: obtaining a pre-stage remote sensing image and a post-stage remote sensing image of a target ecological protection red line region, and performing a data pre-processing; inputting the pre-processed pre-stage remote sensing image and the post-stage remote sensing image into a human activity recognition model after a pre-training; identifying a human activity pattern of the target ecological protection red line region as a first detection result; segmenting, calculating and analyzing the latest image data corresponding to the target ecological protection red line region based on a geographical country situation data to obtain a change pattern as a second detection result; and fusing the first detection result and the second detection result to obtain a change detection pattern of the target ecological protection red line region.

Abstract: In one embodiment, a computing system may access a first image of a first portion of a face of a user captured by a first camera from a first viewpoint and a second image of a second portion of the face captured by a second camera from a second viewpoint. The system may generate, using a machine-learning model and the first and second images, a synthesized image corresponding to a third portion of the face of the user as viewed from a third viewpoint. The system may access a three-dimensional (3D) facial model representative of the face and generate a texture image for the face by projecting at least the synthesized image onto the 3D facial model from a predetermined camera pose corresponding to the third viewpoint. The system may cause an output image to be rendered using at least the 3D facial model and the texture image.

Abstract: In one embodiment, one or more computing systems may access a plurality of images corresponding to a portion of a face of a user. The plurality of images is captured from different viewpoints by a plurality of cameras coupled to an artificial-reality system worn by the user. The one or more computing systems may synthesize, using a machine-learning model, to generate a synthesized image corresponding to the portion of the face of the user. The one or more computing systems may access a three-dimensional (3D) facial model representative of the face of the user and generate a texture image by projecting the synthesized image onto the 3D facial model from a specific camera pose. The one or more computing systems may cause an output image of a facial representation of the user to be rendered using at least the 3D facial model and the texture image.

Abstract: A method for ecological disturbance risk identification and assessment based on automatic parameter adjusting optimization model is disclosed. The method includes: establishing a three-layer ecological disturbance risk assessment index system based on an ecological disturbance risk identification and assessment function, performing normalization preprocessing on the assessment indexes, screening the assessment indexes meeting a multicollinearity judgement interval based on variance inflation factor method, establishing an ecological disturbance risk assessment model, optimizing weight parameters of the ecological disturbance risk assessment model based on particle swarm optimization algorithm to obtain an optimal solution of the model weight, and outputting a result of the risk index of ecological disturbance.

Abstract: In one embodiment, one or more computing systems may access a plurality of images corresponding to a portion of a face of a user. The plurality of images is captured from different viewpoints by a plurality of cameras coupled to an artificial-reality system worn by the user. The one or more computing systems may synthesize, using a machine-learning model, to generate a synthesized image corresponding to the portion of the face of the user. The one or more computing systems may access a three-dimensional (3D) facial model representative of the face of the user and generate a texture image by projecting the synthesized image onto the 3D facial model from a specific camera pose. The one or more computing systems may cause an output image of a facial representation of the user to be rendered using at least the 3D facial model and the texture image.

Abstract: In one embodiment, one or more computing systems may receive an image of a portion of a face of a first user. The one or more computing systems may access a three-dimensional (3D) facial model representative of the face of the first user. The one or more computing systems may identify one or more facial features captured in the image and determine a camera pose relative to the 3D facial model based on the identified one or more facial features in the image and predetermined feature locations on the 3D facial model. The one or more computing systems may determine a mapping relationship between the image and the 3D facial model by projecting the image of the portion of the face of the first user onto the 3D facial model from the camera pose and cause an output image of a facial representation of the first user to be rendered.

Abstract: A human activity recognition fusion method and system for ecological protection red line is disclosed. The method includes: obtaining a pre-stage remote sensing image and a post-stage remote sensing image of a target ecological protection red line region, and performing a data pre-processing; inputting the pre-processed pre-stage remote sensing image and the post-stage remote sensing image into a human activity recognition model after a pre-training; identifying a human activity pattern of the target ecological protection red line region as a first detection result; segmenting, calculating and analyzing the latest image data corresponding to the target ecological protection red line region based on a geographical country situation data to obtain a change pattern as a second detection result; and fusing the first detection result and the second detection result to obtain a change detection pattern of the target ecological protection red line region.

Abstract: A pedestrian re-identification method includes: obtaining a target video containing a target pedestrian and at least one candidate video; encoding each target video segment in the target video and each candidate video segment in the at least one candidate segment separately; determining a score of similarity between the each target video segment and the each candidate video segment according to encoding results, the score of similarity being used for representing a degree of similarity between pedestrian features in the target video segment and the candidate video segment; and performing pedestrian re-identification on the at least one candidate video according to the score of similarity.

Abstract: In one embodiment, one or more computing systems may receive an image of a portion of a face of a first user. The one or more computing systems may access a three-dimensional (3D) facial model representative of the face of the first user. The one or more computing systems may identify one or more facial features captured in the image and determine a camera pose relative to the 3D facial model based on the identified one or more facial features in the image and predetermined feature locations on the 3D facial model. The one or more computing systems may determine a mapping relationship between the image and the 3D facial model by projecting the image of the portion of the face of the first user onto the 3D facial model from the camera pose and cause an output image of a facial representation of the first user to be rendered.

Abstract: A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

Abstract: A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

Abstract: A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

Abstract: Disclosed herein includes a system, a method, and a non-transitory computer readable medium for rendering a three-dimensional (3D) model of an avatar according to an audio stream including a vocal output of a person and image data capturing a face of the person. In one aspect, phonemes of the vocal output are predicted according to the audio stream, and the predicted phonemes of the vocal output are translated into visemes. In one aspect, a plurality of blendshapes and corresponding weights are determined, according to the corresponding image data of the face, to form the 3D model of the avatar of the person. The visemes may be combined with the 3D model of the avatar to form a 3D representation of the avatar, by synchronizing the visemes with the 3D model of the avatar in time.

Abstract: Disclosed herein includes a system, a method, and a non-transitory computer readable medium for applying an expression to an avatar. In one aspect, a class of an expression of a face can be determined according to a set of attributes indicating states of portions of the face. In one aspect, a set of blendshapes with respective weights corresponding to the expression of the face can be determined according to the class of the expression of the face. In one aspect, the set of blendshapes with respective weights can be provided as an input to train a machine learning model. In one aspect, the machine learning model can be configured, via training, to generate an output set of blendshapes with respective weights, according to an input image. An image of an avatar may be rendered according to the output set of blendshapes with respective weights.

Abstract: A pedestrian re-identification method includes: obtaining a target video containing a target pedestrian and at least one candidate video; encoding each target video segment in the target video and each candidate video segment in the at least one candidate segment separately; determining a score of similarity between the each target video segment and the each candidate video segment according to encoding results, the score of similarity being used for representing a degree of similarity between pedestrian features in the target video segment and the candidate video segment; and performing pedestrian re-identification on the at least one candidate video according to the score of similarity.

Abstract: A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

Abstract: The present invention is directed toward novel compounds and novel methods of use of said compounds of the formula (I), useful as nucleocapsid assembly inhibitors for the treatment of viruses, especially but not exclusively, including pregenomic RNA encapsidation inhibitors of HBV for the treatment of Hepatitis B virus (HBV) infection and related conditions. including hydrates, solvates, pharmaceutically acceptable salts, prodrugs and complexes thereof, wherein A, R1, R2, R3, R4, R5, R6, R7, and R8 are defined herein.

Abstract: The present invention provides novel compounds of formula (I) and methods of use thereof. In certain embodiments, the compounds of the invention are useful as nucleocapsid assembly inhibitors. In other embodiments, the compounds of the invention are useful as pregenomic RNA encapsidation inhibitors of Hepatitis B virus (HBV). In yet other embodiments, the compounds of the invention are useful for the treatment of viral infection, including HBV and related viral infections.

Abstract: Pharmaceutical compositions of the invention comprise triazolopyrimidines useful for the treatment of hepatitis virus in a patient.