The present disclosure provides a skill casting method and apparatus of a virtual object, a device, and a storage medium. The method includes displaying a first virtual object in a virtual environment, the virtual environment further comprising a second virtual object, the first virtual object facing a first direction, and the second virtual object being located at a second direction of the first virtual object; displaying a casting preparation process of the first virtual object casting a target skill when the target skill is triggered on the first virtual object; and controlling the first virtual object to cast the target skill toward the second direction when the casting preparation process ends.

This application provides a method and an apparatus for interaction processing of a virtual item, an electronic device, and a computer-readable storage medium. The method includes displaying at least one idle virtual item in a virtual scene; moving a first virtual object in the virtual scene in response to a movement operation on the first virtual object; displaying a pickable prompt of the idle virtual item when there is no obstacle between the idle virtual item and the first virtual object; and controlling the first virtual object to pick up the idle virtual item in response to a picking-up operation by the first virtual object.

This application provides a method and an apparatus for interaction processing of a virtual item, an electronic device, and a computer-readable storage medium. The method includes displaying at least one idle virtual item in a virtual scene; moving a first virtual object in the virtual scene in response to a movement operation on the first virtual object; displaying a pickable prompt of the idle virtual item when there is no obstacle between the idle virtual item and the first virtual object; and controlling the first virtual object to pick up the idle virtual item in response to a picking-up operation by the first virtual object.

A method for generating a colored tridimensional map of an environment surrounding a device, including the steps of receiving a first stream including a plurality of N point cloud frames from a tridimensional sensor, receiving a second stream including a plurality of M images from a camera, generating a global tridimensional map by merging the plurality of N point cloud frames in a reference coordinate system, and determining the colored tridimensional map by assigning a calculated color to each tridimensional data point of global tridimensional map, the calculated color being determined from color values of pixels of the plurality of M images.

A method for generating a colored tridimensional map of an environment surrounding a device, including the steps of receiving a first stream including a plurality of N point cloud frames from a tridimensional sensor, receiving a second stream including a plurality of M images from a camera, generating a global tridimensional map by merging the plurality of N point cloud frames in a reference coordinate system, and determining the colored tridimensional map by assigning a calculated color to each tridimensional data point of global tridimensional map, the calculated color being determined from color values of pixels of the plurality of M images.

A system and methods are provided for large-scale generation of photorealistic 3D models, including training texture map and 3D mesh encoder and decoder neural networks, and training a sampler neural network to convert random seeds into input vectors for the texture map and 3D mesh decoder networks. Training the sampler neural network may include feeding random seeds to the sampler neural network, generating training 3D models from the texture map and 3D mesh decoders, rendering 2D images from the training 3D models, back-propagating output of realism classifier and of a uniqueness function of the 2D images to the sampler neural network; and providing the trained sampler neural network with additional random seed inputs to generate multiple respective input vectors for the texture map and 3D mesh decoders, and responsively generating by the texture map and 3D mesh decoders multiple new 3D models.

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.

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.

Three-dimensional (3D) modeling systems and methods are described for automatically generating photorealistic, virtual 3D package and product models from two-dimensional (2D) imaging assets and dimensional data. The 3D modeling systems and methods include storing, by a memory with one or more processors, 2D imaging assets and dimensional datasets, obtaining, with an imaging asset manipulation script, a shape classification defining a real-world product or product package to be virtually modeled in 3D space, generating, with the imaging asset manipulation script, a spline based on an alpha channel extracted from a 2D imaging asset depicting the real-world product or package, and generating, with the imaging asset manipulation script, a parametric model based on the spline, the dimensional dataset, and the shape classification. A virtual 3D model is generated based on the parametric model and rendered, via a graphical display or environment, as a photorealistic image representing the real-world product or product package.