502results 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.

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.

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.

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.

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.

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.

The disclosed embodiments are directed toward local emotion detection during live streaming events. A client device receives a video stream from a remote server and captures media content while displaying the video stream. The client device uses a local machine learning / artificial intelligence event detection model to detect events in the media content. The client device then transmits detected events to the remote server involved in the live stream. The client device may additionally stream the locally captured media content to the remote server. In response, the remote server provides an interaction dashboard and, in some embodiments, mixes the local media content with the live stream.

Example systems, computer readable medium and methods for automatically generating portrait videos from landscape videos include receiving a landscaped video, identifying one or more objects to track, and tracking the one or more objects by moving a cropping window. A user interface is presented that provides options for a user to automatically convert a landscape video into a portrait video. The options include selecting one or more objects in the landscape video to track and tracking based on a flight plan used to capture the video. The automatic tracking uses a hierarchy of a person for tracking where a face is used if the person is close, an upper body is used if the person is farther away, and a whole body is used if the person is even farther away. A hierarchy of a person includes indications of which parts of the hierarchy to exclude first.

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.

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.

A computer implemented method for generating video content based on natural language input is disclosed. The method includes receiving a natural language instruction describing one or more desired characteristics of a video. A structured script file comprising at least one story beat is generated using a natural language processing engine. A storyboard comprising one or more storyboard frames is created based on the structured script file. One or more virtual components are generated based on the storyboard. An intermediate video sequence comprising a visual component and an auditory component is created using virtual components and the storyboard. The intermediate video sequence is then refined to produce a modified video sequence by applying one or more post-processing effects.

A mobile computing device can be configured, with an improved user interface, to synchronously play audio (or video) and text associated thereto, such as text stored in a synchronization index. Using the synchronization index, the device can periodically compare the current track time with that time associated to a word or range of words, such as a line (or segment) in a plurality of lines of text (or segments of text). Improved navigability of content using an improved mobile computing device and user interface is provided, because a user of the device can scroll through the lines of text associated with the audio or video to find a target word or range of words. If the user selects a particular word or range of words, by making a gesture on the mobile computing device, the device can identify a start time for the selected text. The device can then play the audio or video at the identified start time of the selected text. The improved user interface is a practical application for navigating audio (or video) and text associated thereto on a mobile computing device, providing bimodal reading on mobile computing devices and ease of navigability.

A system is configurable to: after receiving first user input, (i) initiate playback of selected audio content at one or more playback components and (ii) initiate audio recording at one or more recording components to obtain recorded audio content, the recorded audio content being recorded at least partially during the playback of the selected audio content; process the selected audio content and the recorded audio content as inputs to an alignment module to generate a temporal offset value, wherein the alignment module is configured to generate the temporal offset value by correlating feature frames of the selected audio content with feature frames of the recorded audio content; and after receiving second user input, initiate synchronized playback of the selected audio content and the recorded audio content, wherein the recorded audio content is synchronized with the selected audio content using the temporal offset value.

A method, apparatus, non-transitory computer readable medium, and system for generating a video clip includes obtaining an input video and a transcript of the input video, wherein the transcript comprises a plurality of sentences. In some cases, a language model is configured to generate a subset of the plurality of sentences based on the transcript. Additionally, the language model generates a video clip based on the input video and a subset of the plurality of sentences, wherein the video clip comprises a portion of the input video.