665results about "Using detectable carrier information" patented technology

The present disclosure provides systems and methods for video generation corresponding to a user input. Given a user input, a story video with content relevant to the user input can be generated. One aspect includes a computing system comprising a processor and memory. The processor can be configured to execute a program using portions of the memory to receive the user input, generate a story text based on the user input, generate a plurality of story images based on the story text, and output a story including the story text and a story video having content corresponding to the story text, wherein the story video includes the plurality of story images. Additionally or alternatively, the story video can include audio data and a plurality of generated animated videos, each animated video corresponding to a story image in the plurality of story images.

The present disclosure describes techniques for processing monocular videos using three-dimensional gaussian splatting (3DGS). Spatial decomposition and temporal decomposition are performed on a monocular video to generate a plurality of clips. A first set of 3DGS representing foreground objects in each of the plurality of clips are initialized and optimized. A second set of 3DGS representing background in each of the plurality of clips are initialized and optimized. Two images are generated for each frame comprised in each of the plurality of clips based on the first set of 3DGS and the second set of 3DGS, respectively. Two images are merged to generate a resulting image for each frame in each of the plurality of clips. The resulting image accurately represents a corresponding frame in the monocular video.

Embodiments of the present invention provide systems, methods, and computer storage media for cutting down a user's larger input video into an edited video comprising the most important video segments and applying corresponding video effects. Some embodiments of the present invention are directed to adding face-aware scale magnification to the trimmed video (e.g., applying scale magnification to simulate a camera zoom effect that hides shot cuts with respect to the subject's face). For example, as the trimmed video transitions from one video segment to the next video segment, a scale magnification may be applied that zooms in on a detected face at a boundary between the video segments to smooth the transition between video segments.

Embodiments of the present invention provide systems, methods, and computer storage media for identifying the relevant segments that effectively summarize the larger input video and / or form a rough cut, and assembling them into one or more smaller trimmed videos. For example, visual scenes and corresponding scene captions are extracted from the input video and associated with an extracted diarized and timestamped transcript to generate an augmented transcript. The augmented transcript is applied to a large language model to extract sentences that characterize a trimmed version of the input video (e.g., a natural language summary, a representation of identified sentences from the transcript). As such, corresponding video segments are identified (e.g., using similarity to match each sentence in a generated summary with a corresponding transcript sentence) and assembled into one or more trimmed videos. In some embodiments, the trimmed video is generated based on a user's query and / or desired length.

Video processing devices, systems and methods are disclosed. A video processing device for processing a source video stream under the control of a video alteration application is provided. A user interface (UI) is presented via which a user specifies a change to be applied to the source video stream. Conditions for the change to be applied to the source video stream are determined. The content of the source video stream is analysed to determine when conditions for the change have been met, and the location within the source video stream to which the change is to be applied. In response, the source video stream is processed to dynamically apply the specified change at the determined location thereby to generate an output video stream.

A video editing system leverages a generative artificial intelligence (AI) model to identify songs to overlay on a video. The video editing system extracts a set of key frames from the video and prompts the generative AI model to generate a video narrative for the video. A video narrative is a text description of the plot, theme, feel, or other characteristics of the video. The video editing system uses the video narrative to prompt the generative AI model again to generate a set of descriptor tags for the video based on the video narrative. Descriptor tags are strings that represent themes, features, or characteristics of the song. The video editing system uses an audio tagging system to score a set of songs based on the set of descriptor tags and presents a selected subset of the set of songs based on the scores of the songs.

A method of identifying a defensive alignment and an offensive alignment in a set-piece is disclosed herein. A computing system receives one or more streams of tracking data. The computing system identifies a set-piece contained in the one or more streams of tracking data. The computing system identifies a defensive alignment of a first team and an offensive alignment of a second team. The computing system extracts, via a convolutional neural network, one or more features corresponding to a type of defensive alignment implemented by the first team by passing the set-piece through the convolutional neural network. The computing system scans the set-piece, via a machine learning algorithm, to identify one or more features indicative of a type of offensive alignment implemented by the second team. The computing system infers the type of defensive alignment implemented by the first team.

A method for accelerated video editing that includes providing a video, segmenting, using a segmentation module, the video into a number of clips, and automatically computing, for each clip of the number of clips, corresponding metadata representing at least one attribute of the clip based on an analysis of contents of each clip of the plurality of clips. A profile script is generated based on the corresponding metadata, the profile script is generated to include one or more editing actions to be performed on a section of the video that corresponds to at least one identified clip from the number of clips. An editing tool is used to automatically edit the video based on the profile script to generate an edited video.

The present disclosure relates to systems, methods, and non-transitory computer-readable media for segmenting a digital video to segment a digital video by employing a chapterization approach to video transcripts based on contextual data from audio signals and video signals together. In some embodiments, the disclosed systems can extract various types of audio signals and various types of video signals from a digital video. From the extracted signals, the disclosed systems can determine a set of break points to segment a video transcript. In some embodiments, the disclosed systems can further recommend, via a notification on a client device, inserting corresponding breaks from the segmented transcript into the digital video.

Method, system and computer program product for providing an output digital video stream, comprising:continuously collecting a real-time first primary digital video stream;performing a first digital image analysis of the first primary digital video stream to identify at least one first event or pattern, resulting in a first production control parameter being established at a first time delay in relation to a time of occurrence of said first event or pattern in the first primary digital video stream;applying said first production control parameter to said first primary digital video stream, resulting in the first primary digital video stream being modified without being delayed by said first time delay, so as to produce a first produced digital video stream; and continuously providing said output digital video stream based on said first produced digital video stream.

Systems and methods for performing trick play functionality using trick play streams during adaptive bitrate streaming in accordance with embodiments of the invention are disclosed. One embodiment includes requesting a video container index from a video container file containing a video stream from a plurality of alternative streams of video; requesting at least one portion of the video stream using at least one entry from the video container index; decoding the at least one portion of the video stream; receiving at least one user instruction to perform a visual search of the media; requesting a trick play container index from a trick play container file containing a trick play stream; requesting at least one frame of video from the at least one trick play stream; and decoding and displaying the at least one frame of video from the trick play stream.

A system and method for multimedia scene break detection, including: a computer processor, a scene break detection service executing on the computer processor and comprising functionality to: (i) receive a request for scene break detection on the media item, (ii) perform audio break detection on an audio component of the media item to obtain a set of audio break timestamps, (iii) identify a set of video break timestamps, each corresponding to at least one frame of a video component of the media item, (iv) identify a set of candidate scene break timestamps based on the audio break timestamps and the video break timestamps, (v) execute a computer vision scoring model for each candidate scene break timestamp, and (vi) select, based at least on a score of each of the set of candidate scene break timestamps, a final set of scene break timestamps for performing a media action.

This application relates to a method for matching music with a video performed by a computer device, and a storage medium. The method includes: determining a cut speed of a video; determining a long-time audio speed corresponding to each of a plurality of pieces of candidate music according to a high-scale point and a music duration of the candidate music; selecting matched music from the pieces of candidate music according to the cut speed and the corresponding long-time audio speeds; determining, according to a video duration of the video and a high-scale point corresponding to the matched music, a short-time audio speed corresponding to each music clip in the matched music; and determining a target music clip in the matched music according to the cut speed of the video and the corresponding short-time audio speed, and synthesizing the target music clip and the video to obtain a target video.

Disclosed are systems, methods, and devices, that overcome timing and self-expression limitations experienced by vocalists when using prerecorded vocal backing tracks to enhance live performances. The disclosed system, devices, and methods, dynamically synchronizes prerecorded vocal backing tracks with a live vocal stream by extracting vocal elements, such as phonemes, vector embeddings, or vocal audio spectra, from the live vocal performance in real-time. These extracted vocal elements are matched against corresponding timestamped vocal elements previously derived from the prerecorded vocal backing track, enabling precise real-time adjustment and alignment of the backing track timing to the live performance. Additionally, the system enhances expressive performance by identifying prosody factors, such as pitch, vibrato, accent, stress, dynamics, and level, in the live vocal performance, and dynamically adjusting corresponding prerecorded prosody factors within predefined ranges. This maintains naturalness and spontaneity in the vocalist's live performance, overcoming traditional limitations associated with prerecorded vocal backing tracks.

Embodiments of the present invention provide systems, methods, and computer storage media for cutting down a user's larger input video into an edited video comprising the most important video segments and applying corresponding video effects. Some embodiments of the present invention are directed to adding captioning video effects to the trimmed video (e.g., applying face-aware and non-face-aware captioning to emphasize extracted video segment headings, important sentences, quotes, words of interest, extracted lists, etc.). For example, a prompt is provided to a generative language model to identify portions of a transcript (e.g., extracted scene summaries, important sentences, lists of items discussed in the video, etc.) to apply to corresponding video segments as captions depending on the type of caption (e.g., an extracted heading may be captioned at the start of a corresponding video segment, important sentences and / or extracted list items may be captioned when they are spoken).

One variation of a method for serving interactive content to a user includes, at a visual element inserted into a document accessed by a computing device: loading a first frame from a digital video; in response to a scroll-down event that moves the visual element upward from a bottom of a window rendered on the computing device toward a top of the window, seeking from the first frame through a subset of frames in the digital video in a first direction at a rate corresponding to a scroll rate of the scroll-down event, the subset of frames spanning a duration of the digital video corresponding to a length of the scroll-down event; and, in response to termination of the scroll-down event with the visual element remaining in view within the window, playing the digital video forward from a last frame in the subset of frames in the digital video.

Described herein is a computer implemented method for automatically generating a trimmed video clip from a video content item. The method includes: receiving a trim request from a user device, the trim request including the video content item; determining trim parameters for the trimmed video clip, the trim parameters including a trim start time and a trim end time; generating the trimmed video clip based on the trim parameters; and causing display of the trimmed video clip on the user device.

A system and method for contextual advertising that analyzes video content through multimodal examination of visual, audio, and textual elements to create detailed contextual understanding of individual scenes. The system segments video content into discrete scenes and simultaneously processes each scene to extract contextual characteristics including objects, settings, dialogue, music, and emotional tone. These characteristics are classified according to advertising industry taxonomies and converted into numerical embeddings that enable semantic similarity matching. During video playback, when advertisement opportunities occur, the system identifies the current scene context, analyzes available advertisements using similar techniques, computes similarity scores between scene and advertisement characteristics, and selects contextually appropriate advertisements for seamless integration. This approach enables privacy-compliant advertising that matches advertisement content with scene context rather than relying solely on user behavioral data, improving advertisement relevance and viewer experience.

A system and method for programmatic media preview generation, including: a preview generation system executing on a computer processor and configured to receive a request to generate a preview video of a source video file; select a source video for analysis; obtain a set of text metadata comprising groupings of subtitles of the source video; invoke a machine learning model using the set of text metadata to infer a set of candidate previews for the source video file; and provide a final set of candidate previews in response to the request; and a ranking module comprising functionality to rank the set of candidate previews to generate the final set of candidate previews.

Disclosed are systems, servers and methods for providing a novel framework that enables the unique cataloging and organization of audio files, upon which audio rendering experiences can be created and provided to requesting users, whether the users are individuals or third-party partners. The disclosed framework enables audio files to be stripped down, uniquely stored, and then stitched together in a novel manner that previously did not exist within the computing arts. The disclosed systems and methods, therefore, provide a novel platform where audio is not just provided to consumers, but audio experiences are compiled from various types of audio formats and types in a unique, dynamically determined manner for a listening user.

Technology is disclosed for programmatically determining, for a segment of a meeting recording, a user-specific adaptive playback speed, and generating a time-stretched segment playable at the adaptive playback speed. The adaptive playback speed is faster or slower than a default playback speed of the meeting recording. To determine the adaptive playback speed, this disclosure provides technologies to determine a playback data feature based on user-meeting data. The adaptive playback is generated based on the playback data feature. The segment is time-stretched to the adaptive playback speed to generate an updated meeting recording including the segment that is time-stretched and playable at the adaptive playback speed. In this manner, an updated meeting recording, specific to a user, and playable at an adaptive playback speed based on user-meeting data may reduce bandwidth associated with user's manually editing videos or rewinding playback, while improving user experience.

A machine includes a processor and a memory connected to the processor. The memory stores instructions executed by the processor to receive a message and a message parameter indicative of a characteristic of the message, where the message includes a photograph or a video. A determination is made that the message parameter corresponds to a selected gallery, where the selected gallery includes a sequence of photographs or videos. The message is posted to the selected gallery in response to the determination. The selected gallery is supplied in response to a request.

A computer-implemented method for decomposing videos into multiple layers (212, 213) that can be re-combined with modified relative timings includes obtaining video data including a plurality of image frames (201) depicting one or more objects. For each of the plurality of frames, the computer-implemented method includes generating one or more object maps descriptive of a respective location of at least one object of the one or more objects within the image frame. For each of the plurality of frames, the computer-implemented method includes inputting the image frame and the one or more object maps into a machine-learned layer Tenderer model. (220) For each of the plurality of frames, the computer-implemented method includes receiving, as output from the machine-learned layer Tenderer model, a background layer illustrative of a background of the video data and one or more object layers respectively associated with one of the one or more object maps. The object layers include image data illustrative of the at least one object and one or more trace effects at least partially attributable to the at least one object such that the one or more object layers and the background layer can be re-combined with modified relative timings.

A method for accelerated video editing that includes providing a video, segmenting, using a segmentation module, the video into a number of clips, and automatically computing, for each clip of the number of clips, corresponding metadata representing at least one attribute of the clip based on an analysis of contents of each clip of the plurality of clips. A profile script is generated based on the corresponding metadata, the profile script is generated to include one or more editing actions to be performed on a section of the video that corresponds to at least one identified clip from the number of clips. An editing tool is used to automatically edit the video based on the profile script to generate an edited video.

The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.

The invention relates to automatic non-linear editing style transfer. The present disclosure provides systems, methods, and computer program products for performing automatic non-linear editing style transfer. A computer-implemented method may include determining one or more shot boundaries in a video; analyzing the identified content in each of the one or more shots in the video based on performing the object detection; determining an editing style for a respective shot based at least in part on measuring motion across a frame within each of the one or more shots in the video; determining a content segment to be adjusted from the target content set based on analyzing the target content set in consideration of the identified content and the determined editing style from the shot of the video, and automatically adjusting the content segment based at least in part on modifying the content segment from the target set of content with the determined editing style from the shot of the video.

A method generates AI-based video content with a style by capturing scenes in various formats, applying alterations, and training AI with feedback for authenticity. A system includes processors and memory to capture scenes, apply alterations, construct datasets, and train AI for generating styled video content. A non-transitory computer-readable medium has instructions for capturing scenes, applying post-production alterations, and training AI to generate video content with a predetermined style.

A machine includes a processor and a memory connected to the processor. The memory stores instructions executed by the processor to receive a message and a message parameter indicative of a characteristic of the message, where the message includes a photograph or a video. A determination is made that the message parameter corresponds to a selected gallery, where the selected gallery includes a sequence of photographs or videos. The message is posted to the selected gallery in response to the determination. The selected gallery is supplied in response to a request.

The invention provides a video processing method and related equipment. The method comprises the following steps: acquiring attribute information of a target object and an initial video, wherein the initial video comprises audio data about the target object; obtaining a video category and a video effect list of the initial video based on a target text corresponding to the audio data and the attribute information; determining a target material corresponding to the effect object based on the video category and the effect material label; and adding the target material into the original video based on a target timestamp of the text segment in the audio data to obtain a target video.

Methods and systems for managing content items are described. A content item may be separated into components, such as chroma components, luminance components, and audio components. For more efficient management of the content, including for storage and delivery of the content, the separate components may be stored at different locations. For example, chroma components may be stored on a central server, while luminance and audio components are stored at other locations, such as edge servers, or vice versa, to accommodate network efficiency considerations. When content is delivered to a player, two or more separate streams carrying different content components may be delivered to the player.