Abstract: A method for user command-guided editing of an initial textured 3D morphable model of an object comprising: obtaining the initial textured 3D morphable model of the object comprising an initial texture map and an initial 3D mesh model of the object; and determining an edited texture map of the object corresponding to the user command by editing the initial texture map of the object based on a first artificial neural network; and/or determining an edited 3D mesh model of the object corresponding to the user command by editing the initial 3D mesh model of the object based on a second artificial neural network; and generating an edited textured 3D morphable model of the object corresponding to the user command based on the edited texture map of the object and/or the edited 3D mesh model of the object.

Abstract: An image processing circuitry configured to: generate, based on obtained image data, a visual content word sequence indicative for a visual content of an image represented by the obtained image data; and generate, based on the generated visual content word sequence, an image signature for the image.

Abstract: An information processing device, wherein the information processing device includes circuitry configured to: copy, in response to an instruction for play back of a content, encrypted content data; decrypt the copied encrypted content data for obtaining the content data representing the content; and apply a data protection algorithm on the content data to generate protected content data representing protected content, wherein the protected content is played back.

Abstract: The present disclosure relates to an apparatus for image recognition. The apparatus comprises a machine learning network configured to map first and second input image data to either a first or a second predefined target probability distribution, depending on whether the first and second input image data correspond to matching or non-matching images, wherein an output of the machine learning network matching the first target probability distribution is indicative of matching images and an output of the machine learning network matching the second target probability distribution is indicative of non-matching images. The present disclosure also relates to a method for training the apparatus for image recognition.

Abstract: An image processing device is disclosed, featuring interface circuitry to receive image data representing a first image with an aspect ratio smaller than one. This image could be a photograph or a still frame from a video. The device's processing circuitry generates a second image with an aspect ratio greater than one by adding image areas to the lateral sides of the first image. The processing circuitry extends the background into these added areas and identifies foreground objects. If a foreground object is incomplete, the device determines and adds a visual representation of the missing part to complete the object in the extended image areas.

Abstract: An image processing circuitry configured to: store, based on an obtained command, a first image identifier of first image data or the first image data on a write-once-read-many memory, wherein the first image identifier is generated based on the first image data such that it is unique for the first image data.

Abstract: A terminal device (1) comprising circuitry configured to obtain a novel synthetic view based on a position and orientation of the terminal device (1) and to display the novel synthetic view to a user of the terminal device (1).

Abstract: The present disclosure relates to a method for authenticating a user. The method comprises recording image data of the user and deriving at least one first facial feature of the user's face and at least one first gesture feature of one or more gestures of the user from the image data. The method further provides for determining a degree of access of the user to data depending on whether the first gesture feature corresponds to at least one predetermined second gesture feature and whether the first facial feature corresponds to at least one predetermined second facial feature.

Abstract: A method for user command-guided editing of an initial textured 3D morphable model of an object comprising: obtaining the initial textured 3D morphable model of the object comprising an initial texture map and an initial 3D mesh model of the object; and determining an edited texture map of the object corresponding to the user command by editing the initial texture map of the object based on a first artificial neural network; and/or determining an edited 3D mesh model of the object corresponding to the user command by editing the initial 3D mesh model of the object based on a second artificial neural network; and generating an edited textured 3D morphable model of the object corresponding to the user command based on the edited texture map of the object and/or the edited 3D mesh model of the object.

Abstract: An image processing device has circuitry, which is configured to obtain image data, the image data being generated on the basis of a non-linear mapping defining a mapping between an object plane and an image plane; and to process the image data by applying a kernel of an artificial network to the image data based on the non-linear mapping.

Abstract: An image processing circuitry configured to: store, based on an obtained command, a first image identifier of first image data or the first image data on a write-once-read-many memory, wherein the first image identifier is generated based on the first image data such that it is unique for the first image data.

Abstract: A method comprising determining at least one audio event based on an audio waveform and determining a deepfake probability for the audio event.

Abstract: Abstract: An image processing circuitry configured to: generate, based on obtained image data, a visual content word sequence indicative for a visual content of an image represented by the obtained image data; and generate, based on the generated visual content word sequence, an image signature for the image.

Abstract: An information processing device, wherein the information processing device includes circuitry configured to: copy, in response to an instruction for play back of a content, encrypted content data; decrypt the copied encrypted content data for obtaining the content data representing the content; and apply a data protection algorithm on the content data to generate protected content data representing protected content, wherein the protected content is played back.

Abstract: The present disclosure relates to an apparatus for image recognition. The apparatus comprises a machine learning network configured to map first and second input image data to either a first or a second predefined target probability distribution, depending on whether the first and second input image data correspond to matching or non-matching images, wherein an output of the machine learning network matching the first target probability distribution is indicative of matching images and an output of the machine learning network matching the second target probability distribution is indicative of non-matching images. The present disclosure also relates to a method for training the apparatus for image recognition.

Abstract: The present disclosure relates to a method for authenticating a user. The method comprises recording image data of the user and deriving at least one first facial feature of the user's face and at least one first gesture feature of one or more gestures of the user from the image data. The method further provides for determining a degree of access of the user to data depending on whether the first gesture feature corresponds to at least one predetermined second gesture feature and whether the first facial feature corresponds to at least one predetermined second facial feature.

Abstract: Examples relate to a computer-implemented system, a computer-implemented method and a computer program for adapting a machine-learning-architecture, and to a computer-implemented system, a computer-implemented method and a computer program and for processing input data using a machine-learning model having a machine-learning architecture. The computer-implemented system for adapting a machine-learning architecture of a first machine-learning model comprises one or more processors and one or more storage devices. The machine-learning architecture comprises a color image branch configured to process color image data. The color image branch comprises a sequence of convolution blocks. The machine-learning architecture comprises a depth branch configured to process depth data. The depth branch comprises a sequence of convolution blocks. The machine-learning architecture comprises one or more fusing components configured to combine intermediary data of two or more of the branches.

Abstract: An image processing device has circuitry, which is configured to obtain image data, the image data being generated on the basis of a non-linear mapping defining a mapping between an object plane and an image plane; and to process the image data by applying a kernel of an artificial network to the image data based on the non-linear mapping.