A student terminal is for viewing a class given in a virtual space that is immersive. The student terminal includes: a VR function section configured to display the virtual space according to virtual space information; and an input section for receiving a video capturing a desk of a student who views the class. The VR function section extracts, from the video, an area including a top plate of the desk corresponding to a desk object in the virtual space, and performs image composition for fitting a video capturing the area onto a top plate of the desk object.

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.

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.

There is provided an information processing apparatus and method capable of more easily comparing 3D objects. Comparison information which is information for displaying a plurality of 3D objects of 6DoF content in a comparable manner is generated. Further, the plurality of 3D objects of the 6DoF content is displayed in a comparable manner on the basis of comparison information which is information for displaying the plurality of 3D objects in a comparable manner. The present disclosure can be applied to, for example, an information processing apparatus, an information processing method, an information processing system, and the like.

An information processing device 11 including: a control data generation unit that inputs analysis data X that is to be processed, to a trained model that has learnt a relationship between analysis data X that represents a time series of musical notes, and control data Y for controlling movements of an object that represents a performer, thereby generating control data Y according to the analysis data X.

An information processing device according to the present disclosure includes: an acquisition unit that acquires outline information indicating an outline of a user who makes a body motion; and a specification unit that specifies, among body parts, a main part corresponding to the body motion and a related part, which is to be a target of correction processing of motion information corresponding to the body motion, on the basis of the outline information acquired by the acquisition unit.

An information processing device according to the present disclosure includes: an acquisition unit that acquires outline information indicating an outline of a user who makes a body motion; and a specification unit that specifies, among body parts, a main part corresponding to the body motion and a related part, which is to be a target of correction processing of motion information corresponding to the body motion, on the basis of the outline information acquired by the acquisition unit.

In one implementation, a method for generating a blended animation. The method includes: obtaining a motion input vector for a current time period; generating a motion output vector and pose information for the current time period based on the motion input vector; selecting an animated motion from a bank of animated motions for the current time period that matches the pose information within a threshold tolerance value; obtaining a blending coefficients vector for the current time period; generating a blended animation for the current time period by blending the motion output vector with the animated motion based on the blending coefficients vector; and generating a reward signal for the blended animation for the current time period.

In one implementation, a method for generating a blended animation. The method includes: obtaining a motion input vector for a current time period; generating a motion output vector and pose information for the current time period based on the motion input vector; selecting an animated motion from a bank of animated motions for the current time period that matches the pose information within a threshold tolerance value; obtaining a blending coefficients vector for the current time period; generating a blended animation for the current time period by blending the motion output vector with the animated motion based on the blending coefficients vector; and generating a reward signal for the blended animation for the current time period.

A computer-implemented method for processing a set of virtual fibers into a set of clusters of virtual fibers, usable for manipulation on a cluster basis in a computer graphics generation system, may include determining aspects for virtual fibers in the set of virtual fibers, determining similarity scores between the virtual fibers based on their aspects, and determining an initial cluster comprising the virtual fibers of the set of virtual fibers. The method may further include instantiating a cluster list in at least one memory, adding the initial cluster to the cluster list, partitioning the initial cluster into a first subsequent cluster and a second subsequent cluster based on similarity scores among fibers in the initial cluster, adding the first subsequent cluster and the second subsequent cluster to the cluster list, and testing whether a number of clusters in the cluster list is below a predetermined threshold.