A method and system to help control when to monitor for presence of replaceable advertisements in a media stream. An example method includes determining a time offset based at least on a difference between (i) a time of occurrence of a content event in a media stream as indicated by data carried in-band with the media stream and (ii) a time of occurrence of the content event in the media stream as indicated by schedule data that is not carried in-band with the media stream. Further, the method includes applying the determined time offset as a basis to adjust a scheduled time of an advertisement in the media stream, and using the adjusted scheduled time of the advertisement in the media stream as a basis to control when to monitor for presence of a replacement advertisement in the media stream.

To prevent the capture and transmission of excluded data, the current pose and motion of a video camera are used to predict a pose and predicted FOV for the video camera over one or more future frames. The predicted pose and predicted FOV are used to generate cues to enforce an alignment condition to an allowed object or to prevent capture of a disallowed object. If the cues fail, an interrupt is generated to prevent capture of disallowed objects in the video signal and perhaps to deactivate the video camera. The predicted FOV prevents excluded data from entering the video signal and reaching circuitry or being processed downstream of the video camera. This can be implemented in real or near real time.

A content segmentation system includes a computing platform having processing hardware and a system memory storing a software code and a trained machine learning model. The processing hardware is configured to execute the software code to receive content, the content including multiple sections each having multiple content blocks in sequence, to select one of the sections for segmentation, and to identify, for each of the content blocks of the selected section, at least one respective representative unit of content. The software code is further executed to generate, using the at least one respective representative unit of content, a respective embedding vector for each of the content blocks of the selected section to provide a multiple embedding vectors, and to predict, using the trained machine learning model and the embedding vectors, subsections of the selected section, at least some of the subsections including more than one of the content blocks.

Disclosed are example embodiments of systems and methods for displaying a workout session. For example, a method for displaying a workout session is disclosed. The method includes determining whether a plurality of participants are eligible to be presented on a wall of live streams of a live workout session. The method also includes displaying a video stream of a first participant from the plurality of participants on the wall of live streams based on the determination.

Aspects of the disclosure provide methods and apparatuses for media processing. In some examples, an apparatus includes processing circuitry. The processing circuitry can exchange, with a server device, a plurality of control messages over a control plane channel that uses a first transport protocol. The plurality of control messages belongs to a control plane of a bidirectional protocol for immersive media distribution. The processing circuitry receives, from the server device, a first plurality of data messages over a first data plane channel that uses a second transport protocol. The first plurality of data messages belongs to a data plane of the bidirectional protocol and carries immersive media content. The processing circuitry presents the immersive media content carried by the first plurality of data messages.

A method, a system, and a computer program product for adapting video content to mitigate adverse health effects in users. A data file uploaded to a first storage location is detected. The data file is tagged upon determining a presence of one or more triggering content. At least one of a location and a type of the triggering content in the data file are determined. One or more timestamps identifying the location of the triggering content are inserted in the data file. A modified data file is generated and a playback of the modified data file is executed.

Example methods and systems for modifying the playback of content using pre-processed profile information are described. Example instructions, when executed, cause at least one processor to access a media stream that includes media and a profile of equalization parameters, the media stream provided to a device via a network, the profile of equalization parameters included in the media stream selected based on a comparison of a reference fingerprint to a query fingerprint generated based on the media, the profile of equalization parameters including an equalization parameter for the media; and modify playback of the media based on the equalization parameter specified in the accessed profile.

Systems, methods and non-transitory computer readable media for generating personalized videos from textual information are provided. An indication of a preference of a user is obtained. Further, textual information for generating a personalized video is obtained from the user. At least one characteristic of a character is selected based on the preference of the user. An artificial neural network, the textual information and the selected at least one characteristic of the character is used to generate the personalized video depicting the character with the selected at least one characteristic.

Aspects described herein may allow an automated generation of an interactive multimedia content with annotations showing vehicle damage. In one method, a server may receive vehicle-specific identifying information of a vehicle. Image sensors may capture multimedia content showing aspects associated with exterior regions of the vehicle, and may send the multimedia content to the server. For each of the exterior regions of the vehicle, the server may determine, using a trained classification model, instances of damage. Furthermore, the server may generate an interactive multimedia content that shows images with annotations indicating instances of damage. The interactive multimedia content may be displayed via a user interface.

Aspects of the subject disclosure may include, for example, a method that includes obtaining, by a processing system including a processor, video frames over a network; the processing system uses a machine learning algorithm to identify in each frame a first region comprising a natural image and a second region comprising a synthetic graphic image. The processing system separates the natural image from the synthetic graphic image to generate a natural video and a graphics video, encodes the natural video, and processes the graphics video to generate instructions for rendering graphic images at a client system. The client system performs a decoding procedure for the encoded video, a rendering procedure for client-side graphics in accordance with the instructions, and a compositing procedure to obtain a presentable video stream including the natural image and a client-side graphic corresponding to the synthetic graphic image. Other embodiments are disclosed.