A method for controlling animation refreshing, a computer device, and a storage medium are provided. The method includes the following. An animation-refreshing request of an animation is received, where the animation-refreshing request instructs a processor to perform animation refreshing corresponding to the animation-refreshing request. In response to a current time reaching a preset refreshing time, at least one target animation-refreshing request in a time period from the current time to a last refreshing time is obtained from at least one received animation-refreshing request. The at least one target animation-refreshing request is sent to the processor.

Embodiments of this application provide a virtual scene display method performed at a computing device. The method includes: obtaining animation data and video data; displaying an animation interface corresponding to the animation data in an animation display area of a display interface, the animation interface including a first virtual scene including a plurality of animation elements; and playing a video clip in the video data in response to an operation on the animation elements in the animation interface, the video clip presenting a picture of transformation from the first virtual scene to a second virtual scene. A virtual scene display solution provided in the embodiments of this application can reduce resource consumption of an intelligent device and reduce picture stutter.

Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more neural networks are used to generate one or more images of one or more objects based, at least in part, on input indicating motion of the one or more objects.

Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more neural networks are used to generate one or more images of one or more objects based, at least in part, on input indicating motion of the one or more objects.

Method for generating media content items on demand starts with a processor receiving an animation file including a first metadata based on an animation input. The animation file is associated with a media content identification. The processor generates puppets associated with frames in the animation file using the first metadata. The processor causes a puppet matching interface to be displayed on a client device. The puppet matching interface includes one of the puppets in a first pose. The processor receives a puppet posing input associated with a second pose from the client device. The processor causes the one of the puppets to be displayed in the second pose in the puppet matching interface by the client device. The processor can also generate a second metadata based on the puppet posing input. Other embodiments are disclosed herein.

This disclosure covers methods, computer-readable media, and systems that animate a digital graphic associated with a video or other visual media item based on a detected dynamic attribute. In particular, the disclosed methods, computer-readable media, and systems detect sensor data from a client device or a motion of an object within a video or other visual media item. Based on the detected sensor data or motion of an object within a visual media item, the methods, computer-readable media, and systems overlay and animate an emoji or other digital graphic selected by a user on a video or other visual media item.

This disclosure covers methods, computer-readable media, and systems that animate a digital graphic associated with a video or other visual media item based on a detected dynamic attribute. In particular, the disclosed methods, computer-readable media, and systems detect sensor data from a client device or a motion of an object within a video or other visual media item. Based on the detected sensor data or motion of an object within a visual media item, the methods, computer-readable media, and systems overlay and animate an emoji or other digital graphic selected by a user on a video or other visual media item.

Robust temporal gradients, representing differences in shading results, can be computed between current and previous frames in a temporal denoiser for ray-traced renderers. Backward projection can be used to locate matching surfaces, with the relevant parameters of those surfaces being carried forward and used for patching. Backward projection can be performed for each stratum in a current frame, a stratum representing a set of adjacent pixels. A pixel from each stratum is selected that has a matching surface in the previous frame, using motion vectors generated during the rendering process. A comparison of the depth of the normals, or the visibility buffer data, can be used to determine whether a given surface is the same in the current frame and the previous frame, and if so then parameters of the surface from the previous frame G-buffer is used to patch the G-buffer for the current frame.

Biometric enabled virtual reality (VR) systems and methods are disclosed for detecting user intention(s) and modulating virtual avatar control based on the user intention(s) for creation of virtual avatar(s) or object(s) in holographic space, two-dimensional (2D) virtual space, or three-dimensional (3D) virtual space. A virtual representation of an intended motion of a user corresponding to an intention of muscle activation of the user is determined based on analysis of a biometric signal data of the user as collected by a biometric detection device. The virtual representation of the intended motion is used to modulate virtual avatar control or output to create at least one of a virtual avatar representing aspect(s) of the user or an object manipulated by the user in a holographic space, virtual 2D space, or virtual 3D space. The avatar or the object is created based on: (1) the biometric signal data of a user, or (2) user-specific specifications as provided by the user.

Systems and methods are disclosed for generating, a source image sequence using an image sensor of the computing device, the source image sequence comprising a plurality of source images depicting a head and face, identifying driving image sequence data to modify face image feature data in the source image sequence, generating, using an image transformation neural network, a modified source image sequence comprising a plurality of modified source images depicting modified versions of the head and face, and storing the modified source image sequence on the computing device.