Rittner, Karsten

An encoding method is disclosed. An avatar animation unit (AAU) is obtained that comprises a header and a payload. From the header, it is determined whether the AAU is a keyframe AAU or a non-keyframe AAU. The AAU is finally encoded responsive to the determining.

The present principles generally relate to the storage and transmission of avatar texture animation data.

Systems and method directed to point cloud deep learning inpainting of projections. One example is directed to a computer-implemented method for video encoding or decoding including deriving non-projected points from decoded geometry of a three-dimensional -3D-point cloud, predicting attributes for the non-projected points using a convolutional neural network, and encoding or decoding the 3D point cloud based on the attributes.

Methods, apparatus and data stream are provided for encoding and decoding a three-dimensional (3D) point cloud. The 3D point cloud is processed to obtain a coded geometry, a reconstructed geometry obtained by decoding the coded geometry and adapted attributes. The adapted attributes are obtained by tracking indices of points of the point cloud or by transferring attributes to the reconstructed geometry. The 3D point cloud is divided in a regular grid of blocks, projecting adapted attributes of points of each non-empty block on inner faces of the blocks and selecting one of the inner faces to obtain patches. The patches are packed in an atlas image according to a scanning process and the atlas image is encoded in a data stream with metadata indicating selected inner faces.

Methods, apparatus and data stream are provided for managing avatars in a 3D scene. Avatar skeleton comprising joint landmarks are encoded in a data stream. A set of joints landmarks for a given function. This function may be relative to a location of the joint landmarks of the set, like the positioning of the avatar in the 3D scene. The function may also be relative to a weighted sum of a location of the joint landmarks of the set, like the determining of a mass center.

Examples are provided herein of combining measurements and estimates for carbon emission reporting. In an example, a content provider transmits a video segment to a first set of display devices and a second set of display devices. The content provider receives energy consumption reports from the first set of display devices, each energy consumption report including energy consumption for processing and display of the video segment by a respective display device of the first set of display devices. Further, the content provider aggregates the energy consumption reports for the first set of display devices to obtain a first total energy consumption. Also, the content provider estimates a second total energy consumption for the second set of display devices. Moreover, the content provider combines the first total energy consumption and the second total energy consumption into a final total energy consumption.

Methods and apparatus for associating energy use with a data-stream passing through a device, or system or service center are provided, enabling reporting and control of energy from data centers or content delivery networks. In one embodiment, information is transmitted from a data center, comprising a number of customers associated with the data center, corresponding data rates for each of the number of customers and an energy for each of the number of customers. A total energy can be determined for all customers and for individual users. In another embodiment, an energy corresponding to an individual user within a set time interval can be determined and transmitted using the determined energy corresponding to the data center.

Extra precision can be added to motion vectors, such as those obtained using advanced motion vector prediction or frame rate upconversion, to produce improved motion compensation. The increased precision from neighboring blocks are used to provide increased precision to a motion vector for a current block. In various embodiments, the derivation of the increased precision can be found through various methods. In other embodiments, refined motion vectors can be saved for use in subsequent predictions, and a rate distorion optimized selection can be performed for determining whether or not to use the refinement.

Some embodiments of a method may include: obtaining Avatar JSON Interchange File (AJIF) data; decoding the AJIF data to generate avatar data and associated neural radiance field properties; obtaining user movement data corresponding to a movement of a user; generating updated avatar data based on the user movement data by using a neural networked configured with the neural radiance field properties; and sending the updated avatar data to a client device. Another method may include: generating AJIF data corresponding to an avatar; sending, to an application server, the AJIF data; obtaining user movement data corresponding to a movement of a user; sending, to the application server; receiving, from the application server, scene update data, wherein the scene update data includes updated avatar data corresponding to the movement of the user; and rendering a scene update based on the scene update data.

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