1114results about "Carrier indicating arrangements" patented technology

In some embodiments, a computer system displays a caption for a media item at different depths depending on the depth of the portion of the media item over which the caption is displayed. In some embodiments, a computer system displays a user interface element that includes information associated with the media item at different locations relative to the media item depending on attention of the user. In some embodiments, a computer system displays a user interface element that includes information associated with the media item with different visual appearances depending on visual characteristics of the portion of the media item over which the user interface element is displayed.

Provided is a video generation model for performing text-to-video (T2V) or other video generation techniques. The proposed model reduces the computational costs associated with video generation. In particular, unlike traditional T2V methods, the disclosed technology can generate the full temporal duration of a video clip at once, bypassing the need for extensive computation. As one example, a machine-learned denoising diffusion model can simultaneously process a plurality of noisy inputs that correspond to various timestamps spanning the temporal dimension of a video to simultaneously generate synthetic frames for the video that match the timestamps.

A system, a method and a processor are for AI-powered generation and delivery of video clips. The processor is configured to: load a first video file of a first video content item, the first video file comprising video frames associated with timestamps; load a first subtitle file of the first video content item, the first subtitle file comprising subtitle text associated with the timestamps; execute a natural language processing (NLP) model with the subtitle text as input, the NLP model including language pre-processing steps for classifying words, names or phrases in the subtitle text and associating initial classifiers with the subtitle text, the NLP model including one or more of a recurrent neural network (RNN), a Bidirectional Encoder Representations from Transformers (BERT) model, or a generative pre-trained transformer (GPT) model for a dialogue analysis comprising processing sequences of dialogue in the subtitle text in view of the initial classifiers to associate one or more portions of the dialogue with one or more first classifiers of first narrative elements; execute an image recognition model with at least some of the video frames as input, the image recognition model including a convolutional neural network (CNN) for an object detection analysis and a facial recognition analysis comprising processing video sequences to associate one or more of the video frames with one or more second classifiers of second narrative elements; generate a narrative map of the first video content item by temporally aligning the first narrative elements with the second narrative elements based on the timestamps associated with the video frames and the first subtitle file; and generate a video clip including at least one segment of the first video content item, the at least one segment including selected video frames associated with at least one of the first or second narrative elements identified from the narrative map and selected for inclusion in the video clip.

Methods and systems are disclosed for generating video by applying a template to various content items. The methods and systems select, by an interaction application, a video generation template comprising instructions for combining a set of content items into a video using one or more augmented reality (AR) elements. The methods and systems identify a subset of content items from a plurality of previously captured content items and modify one or more content items of the identified subset of content items based on the AR elements of the video generation template. The methods and systems generate a video comprising a collection of content items including the identified subset of content items and the modified one or more content items based on the instructions of the video generation template.

A video having a wide field of view (e.g., spherical field of view) may be presented within a graphical user interface. The graphical user interface may include multiple punchouts of the wide field of view, with the number of punchouts set based on user interaction with a punchout-number element of the graphical user interface. Individual punchouts may be used as output of a virtual camera to provide views of differential spatial parts of the video. Presentation of different spatial parts of the video within different punchouts may be automatically synchronized based on the different spatial parts originating from a single video.

A user interface change event is detected. An app identifier of an associated third party app is determined. A dynamic rule data structure associated with the app identifier of the associated third party app is determined. A user interface match state specified by the dynamic rule data structure is determined based on absolute screen positioning or relative screen positioning. A current user interface state of the associated third party app is determined by querying an accessibility API. Upon determining that the user interface match state matches the current user interface state, at least one user interface report field specified by the dynamic rule data structure is determined and at least one report field property of the at least one user interface report field is extracted by querying the accessibility API. An active session data structure associated with the dynamic rule data structure is updated to store the extracted property value.

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.

An audio processing method implemented by an electronic device includes entering a multi-channel video recording mode, detecting a shooting operation of a user, simultaneously recording, after detecting the shooting operation, a first video image and a second video image using a first camera and a second camera, and recording audio of a plurality of sound channels, where the audio includes panoramic audio, first audio corresponding to the first video image, and second audio corresponding to the second video image. The electronic device further records the first audio based on a feature value such as a zoom magnification corresponding to the first display area.

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.

Provided are a method and an apparatus for generating a video, a device, and a medium. In one method, a first reference image and a second reference image are determined from a plurality of reference images in a reference video. A reference text for describing the reference video is received. A generation model is acquired based on the first reference image, the second reference image and the reference text. The generation model is configured to generate a target video based on a first image, a second image and a text. With example implementations of the present disclosure, the second reference image can serve as guiding data to determine a development direction of a story in the video. In this way, the generation model can clearly grasp changes of various image contents in the video, which is beneficial to generating richer and more realistic videos.

A method includes receiving a series of video segments and providing the series of video segments as input to a first machine learning model to produce text data. The text data is provided as input to a second machine learning model to produce categorized text data that includes a classification indication. The classification indication is added to metadata of the video segment, and the categorized text data is provided as input to a third machine learning model to produce a semantic vector. The method also includes causing the video segment and the metadata that includes the classification indication to be stored at a location of a database based on the semantic vector, the database being configured to be searched based on a search query associated with the semantic vector.

The subject technology receives frames of a source media content. The subject technology detects from the frames of the source media content, a first gesture indicating a cut point at a particular frame of the source media content, the cut point associated with a trimming operation to be performed on the source media content. The subject technology selects a starting frame and an ending frame from the frames based at least in part on the cut point at the particular frame. The subject technology performs the trimming operation based on the starting frame and the ending frame. The subject technology generates a second media content using the third set of frames. The subject technology provides for display at least a portion of the third set of frames of the second media content.

A method to highlight video segments in a video stream, where the method includes receiving a video stream from a video source, identifying a highlight segment within the video stream based on a machine learning model, the highlight segment being deemed to be worthy of replay by the machine learning model, and starting and ending frames of the highlight segment being identified by applying the machine learning model to the video stream and corresponding audio data, and providing an availability indication of the highlight segment in the video stream once the starting and ending frames of the highlight segment are identified.

The subject technology receives frames of a source media content. The subject technology detects from the frames of the source media content, a first gesture indicating a cut point at a particular frame of the source media content, the cut point associated with a trimming operation to be performed on the source media content. The subject technology selects a starting frame and an ending frame from the frames based at least in part on the cut point at the particular frame. The subject technology performs the trimming operation based on the starting frame and the ending frame. The subject technology generates a second media content using the third set of frames. The subject technology provides for display at least a portion of the third set of frames of the second media content.

Methods and apparatus are described for evaluating dubbing of media content. Phonemes in dubbed audio are extracted and mapped to visemes. Lip poses in video frames of the media content corresponding to the phonemes of the dubbed audio are compared to the visemes determined from the dubbed audio. A notification may be generated based on the comparison that indicates synchronization of the dubbed audio to lip poses of the video.

Embodiments of this application provide a video processing method and an electronic device. The method includes: The electronic device displays a first interface. In response to a tap operation performed by a user on a first option on the first interface, the electronic device displays a second interface. In response to a first operation performed by the user on a first control on the second interface, the electronic device generates a first finished video. The electronic device displays a third interface. In response to a tap operation performed by the user on the first option on the third interface, the electronic device displays a fourth interface. In response to the first operation performed by the user on the first control on the fourth interface, the electronic device generates a second finished video. The first finished video and the second finished video have different video effects.

Techniques are described for using computing devices to perform automated operations for automatically generating videos and associated information about a building interior using other visual data about the building interior, as well as presenting the generated videos and associated information in various manners. In some situations, the generation is based at least in part on user input provided via user interactions with a displayed floor plan of the building, such as to select one or more rooms or other areas for which to include visual data in the video, and / or to select one or more building objects and / or other building structural elements and / or other building attributes for which to include visual data in the video. The techniques may further include determining and using information about such building attributes of the building from automated analysis of building information that includes floor plans and acquired building images.

A system, a method and a processor are for AI-powered generation and delivery of video clips. The processor is configured to: load a first video file of a first video content item, the first video file comprising video frames associated with timestamps; load a first subtitle file of the first video content item, the first subtitle file comprising subtitle text associated with the timestamps; execute a natural language processing (NLP) model with the subtitle text as input, the NLP model including language pre-processing steps for classifying words, names or phrases in the subtitle text and associating initial classifiers with the subtitle text, the NLP model including one or more of a recurrent neural network (RNN), a Bidirectional Encoder Representations from Transformers (BERT) model, or a generative pre-trained transformer (GPT) model for a dialogue analysis comprising processing sequences of dialogue in the subtitle text in view of the initial classifiers to associate one or more portions of the dialogue with one or more first classifiers of first narrative elements; execute an image recognition model with at least some of the video frames as input, the image recognition model including a convolutional neural network (CNN) for an object detection analysis and a facial recognition analysis comprising processing video sequences to associate one or more of the video frames with one or more second classifiers of second narrative elements; generate a narrative map of the first video content item by temporally aligning the first narrative elements with the second narrative elements based on the timestamps associated with the video frames and the first subtitle file; and generate a video clip including at least one segment of the first video content item, the at least one segment including selected video frames associated with at least one of the first or second narrative elements identified from the narrative map and selected for inclusion in the video clip.

In some embodiments, a method of processing image data may include receiving environmental data and associated capture time data from a sensor of a mobile computing device, the capture time data reflecting capture time of the environmental data; processing the environmental data to generate metadata; time stamping the metadata using the capture time data; receiving video data and video time data at a processor; correlating the metadata to the video data using the capture time data and the video time data; receiving a search query; and / or identifying a frame within the video data by performing a search of the metadata using the search criterion.

The embodiment of the invention discloses a video stream mixing method and device, electronic equipment and a storage medium. According to the embodiment of the invention, an editing interface comprising a multi-track area can be displayed, the multi-track area comprises a video track and a dubbing subtitle track, and a target video is loaded in the video track; in response to the starting dubbing operation, starting to record audio at the first progress of the dubbing subtitle track; in response to the dubbing ending operation, generating a dubbing fragment and an audio recognition text corresponding to the dubbing fragment; a mixed stream video is generated in response to the stream mixing operation. According to the invention, a rapid flow mixing mode for adding dubbing and corresponding subtitles at the same time is provided, manual configuration is not needed, a user only needs to input the voice, the audio recognition text corresponding to the voice can be directly displayed in the dubbing subtitle track as the subtitles when recording is completed, and the voice and the audio recognition text are mixed into the video during flow mixing. Therefore, according to the scheme, the operation complexity of the video stream mixing method can be reduced.

Methods, systems, and computer-readable storage media for a video generation platform that automatically generates videos, also referred to herein as stories, based on story templates, story data, and story metadata. The video generation platform provides interfaces for third-party systems to render videos and publish the videos as a story for defined channels and recipients.

The method comprises: in response to a special effect triggering operation, sequentially collecting to-be-processed video frames; when a touch point on a display interface is detected, determining a target display position of the touch point in the to-be-processed video frames, and adding a target control object at the target display position; when a special effect playing condition is detected, sequentially determining display forms of the target control object; and controlling the target control object to display according to the corresponding display forms in the to-be-processed video frames. The technical solution provided in the embodiments of the present disclosure can determine the placement position of the target control object based on the triggering operation of the user, that is, the interaction effect between the user and the display interface is achieved. Furthermore, on the basis of meeting the special effect playing condition, each target control object can be controlled to display according to the corresponding display form, thereby improving the richness of the picture content.

The present disclosure relates to a video editing template search method, device, electronic device, and storage medium, in which a search keyword entered by a user is obtained, and matching is performed based on the search keyword to obtain target music that matches the search keyword and a first template video edited using the target music, and then the first template video and the target music are presented to the user in the form of a card on a search result page.