A face image processing method and apparatus, a face image display method and apparatus, and a device are provided, belonging to the technical field of image processing. The method includes: acquiring a first face image of a person; invoking an age change model to predict a texture difference map of the first face image at a specified age, the texture difference map being used for reflecting a texture difference between a face texture in the first face image and a face texture of a second face image of the person at the specified age; and performing image processing on the first face image based on the texture difference map to obtain the second face image.

A sheet providing method provides a sheet that is used by being applied to a body surface of an individual user. The method involves determining a shape and dimensions of a sheet for each individual user U based on body surface information of the individual user; and forming the sheet by controlling a discharge nozzle that discharges a raw material of the sheet based on information regarding the shape and the dimensions of the sheet. Also, a sheet providing system having a sheet specification determining unit that determines a shape and dimensions of a sheet for each individual user based on body surface information of the individual user; and a sheet forming unit that forms the sheet by controlling a discharge nozzle that discharges a raw material of the sheet based on information regarding the shape and the dimensions of the sheet.

Systems and methods are described herein to determine positive or negative displacement distances for each pixel of an image of a graphical element. A displacement subsystem may determine surface displacement distances based on a function of a distance of each pixel to a nearest edge pixel of the image of the graphical element. A mapping subsystem may generate a surface displacement map of the graphical element to be applied to a surface of a three-dimensional object. The surface displacement map may be used to generate a mesh file and/or transmitted to a three-dimensional printing for printing on a surface of an object.

An image processing method, an electronic device and a computer storage medium are provided, which relates to the fields of computer vision, augmented reality and artificial intelligence technologies. An implementation includes: acquiring a to-be-processed face image; reconstructing a face based on the to-be-processed face image to obtain a first blend coefficient vector based on a first blendshape base group; mapping the first blend coefficient vector to a second blendshape base group according to a pre-obtained coefficient mapping matrix between the first blendshape base group and the second blendshape base group to obtain a second blend coefficient vector based on the second blendshape base group; acquiring input face adjustment information, the face adjustment information including second blendshape base information; and obtaining a target face image based on the second blendshape base information and the second blend coefficient vector.

An image registration apparatus including at least one processor and configured to project, to a first model, a first image generated based on an image obtained from a first camera to generate a first intermediate image, to map the first intermediate image to a first output model to generate a first output image, to project, to a second model, a second image generated based on an image obtained from a second camera to generate a second intermediate image, to map the second intermediate image to a second output model to generate a second output image, and to determine a match rate between the first output image and the second output image and transform at least one of the first model and the second model based on a determined match rate and a preset reference match rate.

The disclosed systems, components, methods, and processing steps are directed to determining user-item fit characteristics of an item for a user body part by accessing a three-dimensional (3D) reconstructed model of the user body part, accessing information about one or more 3D reference models of the item, the information for each 3D reference model including respective dimensional measurement, spatial, and geometrical attributes, performing a 3D matching process based on the 3D reconstructed model and the accessed information of the one or more 3D reference models to determine a best-fitting 3D reference model from the one or more 3D reference models, integrating the best-fitting 3D reference model with the 3D reconstructed model to provide a 3D best fit representation and displaying the 3D best fit representation along with visual indications of user-item fit characteristics.

Embodiments of the present technology are directed at features and functionalities of a VR/AR enabled digital audio workstation. The disclosed audio workstation can be configured to allow users to record, produce, mix, and edit audio in virtual 3D space based on detecting and manipulating human gestures in a virtual reality environment. The audio can relate to music, voice, background noise, speeches, background noise, one or more musical instruments, special effects music, electronic humming or noise from electrical/mechanical equipment, or any other type of audio.

When three-dimensional audio is produced by using headphones, particular HRTF-filters are used to modify sound for the left and right channels of the headphone. As the morphology of every ear is different, it is beneficial to have HRTF-filters particularly designed for the user of headphones. Such filters may be produced by deriving ear geometry from a plurality of images taken with an ordinary camera, detecting necessary features from images and fitting said features to a model that has been produced from accurately scanned ears comprising representative values for different sizes and shapes. Taken images are sent to a server (52) that performs the necessary computations and submits the data further or produces the requested filter.

Systems and methods are disclosed for treating teeth to correct for malocclusions. This may be accomplished by applying a series of labels to a digital dental model and applying a rolling ball process to identify tooth boundaries separating one tooth from a neighboring tooth and to also determine the crown/gum margin. The user may further assign regions to the dental model to indicate hard regions and soft regions. With the dental model labeled and defined, the user may then generate a treatment plan for moving the labeled and defined tooth or teeth relative to one another to correct for any malocclusions. Upon approval of the treatment plan, a series of 3D printed dental appliances or aligners to be worn in series by the patient may be fabricated to ultimately move the tooth or teeth to a desired position.

A computer-implemented method for designing a 3D modeled object via user-interaction. The method includes obtaining the 3D modeled object and a machine-learnt decoder. The machine-learnt decoder is a differentiable function taking values in a latent space and outputting values in a 3D modeled object space. The method further includes defining a deformation constraint for a part of the 3D modeled object. The method further comprises determining an optimal vector. The optimal vector minimizes an energy. The energy explores latent vectors. The energy comprises a term which penalizes, for each explored latent vector, non-respect of the deformation constraint by the result of applying the decoder to the explored latent vector. The method further includes applying the decoder to the optimal latent vector. This constitutes an improved method for designing a 3D modeled object via user-interaction.