48 results about "Film editing" patented technology

The application discloses a kind of video intelligent slice method based on multi-modal information fusion and semantic constraint, it is related to video content analysis and intelligent editing technical field, it is characterized in that it proposes semantic priority audiovisual collaborative slice scheme, obtains shot interval by shot boundary detection, constructs voice interval and effective word information by voice recognition, adopts voice interval as semantic mask to remove visual cut point in the process of voice expression, and based on fragment length and effective word, content density determination is carried out to long fragment, and further adopts visual re-cutting, audio energy driving and uniform cutting Secondary slicing strategy of division, the method provided by the application solves the problem of semantic fragmentation and content perception loss in the prior art, can obtain video fragment with complete semantics, high content density and appropriate length, and is suitable for various application scenarios such as automatic editing, short video generation and video content retrieval.

The application relates to a kind of adaptive video streaming method and system based on serverless computing, belong to streaming media transmission technical field.System is realized by fine-grained serverless pipeline Video delivery, use stateless function to strengthen the response to video request event, use a kind of near-end strategy optimization PPO of three-end clipping based on deep reinforcement learning algorithm to solve the bit rate adaptive sequence decision problem in video playing process.In addition, dynamic video block quality factor is included in user experience QoE index, to configure QoE model, for each video block is assigned a priority weight, improves the robustness of video bit rate decision, thereby reduces video streaming delay, improves user viewing experience.

This application relates to the field of computer application technology and provides a template matching method and electronic device for editing footage. The method includes: acquiring at least one piece of footage to be edited selected by a user, wherein the footage to be edited includes at least one of video and image; identifying themes of the footage to be edited based on various template themes contained in a template library to obtain multiple templates to be matched from the template library; matching each template to be matched with the footage to be edited according to at least one preset matching index to determine the target matching score corresponding to each template to be matched; and selecting at least one template to be matched as the target template corresponding to the footage to be edited based on the target matching score corresponding to each template to be matched. Therefore, by evaluating the matching degree between templates and footage to be edited from multiple evaluation dimensions, the matching degree between recommended templates and user-selected footage is improved, the final product effect is enhanced, and the user experience is improved.

The present disclosure provides a multimedia resource processing method, device and equipment and storage medium, the method comprises: first, receiving the light source adding operation for the target processing image, wherein the target processing image comprises one or more frames of image, the target processing image on which a first virtual light source is shown is displayed on a video clip page, wherein the first virtual light source is generated based on the light source adding operation, the video track is displayed on the video clip page, and the target processing image corresponds to all or part of the image frames in the video track. It can be seen that the embodiment of the present disclosure can support the function of adding a virtual light source to the target processing image on the video clip page, so as to improve the display effect of the target processing image, thereby improving the user's editing experience.

The application discloses a kind of family audio-video imprint automatic aggregation and personal biography film generation method and system based on genealogy figure. First, structured family knowledge base including family member node, blood relationship edge and associated multimedia data is constructed. In response to biography generation request, start multi-modal figure imprint aggregation: fusion face recognition and blood relationship auxiliary matching, automatically identify and extract all image fragments of target figure appearance from audio-video library, form cross-modal material set. Further, blood narrative guided intelligent editing is carried out: based on age estimation, material time sequence segmentation is carried out, narrative structure is automatically generated in combination with blood event node, and material enhancement technology is used to output into film. The system automatically generates the "family imprint" column in personal data card, and aggregates and displays related audio-video materials. Through integrated path, scattered family memories are gathered into personal image biography, which greatly reduces the production threshold, realizes the automatic aggregation and generation of family memories.

The application relates to the technical field of video processing, and discloses a deep learning-based automatic video editing method and medium, wherein the deep learning-based automatic video editing method comprises the following steps: analyzing video shot composition quality by using a multi-dimensional aesthetic feature contrast learning network; using a multi-category composition evaluation network and a diffusion model to repair composition defects and generate optimized shot content; decomposing the optimized shot content into a geometric layer, a material layer, an illumination layer and an environmental reflection layer by using a deep neural network; using a multi-scale neural BRDF network to establish a material reflection characteristic model of each point in a scene, so that accurate material representation and reconstruction are realized; combining a path tracking algorithm and a neural network to realize visual style enhancement; and generating final high-quality video content through space-time condition control and space-time consistency loss; and the application realizes automatic editing and style enhancement of high-quality videos.

This disclosure relates to the field of computer technology and discloses a video editing method, apparatus, electronic device, storage medium, and program product. The method includes: acquiring video material of a target object and prompting information; the video material includes video data, and the prompting information includes descriptive information of the target object and video editing requirements; performing shot segmentation on the video data to obtain multiple video segments; performing frame extraction on each video segment to obtain multiple target video frames; inputting the prompting information and each target video frame into a target language model for video editing processing to obtain target editing information, which includes target video track information and target spoken script information; and generating an edited video of the target object based on the target editing information. This disclosure can solve the problem of low video editing efficiency.

The application provides a cloud video processing method and system and a storage medium, and the method comprises the following steps: acquiring cloud video data uploaded by a user, determining a clip element and a clip template according to a clip requirement of the user, performing clip processing on the cloud video data according to the clip element and the clip template, and obtaining a clip cloud video; acquiring annotation information for the clip cloud video, performing annotation on the clip cloud video according to the annotation information, and obtaining an annotated cloud video; acquiring a modified cloud video of the annotated cloud video, performing video classification on the modified cloud video when the modified cloud video meets a film review requirement, and obtaining a video type; determining a video push plan according to the video type, and performing video push on the modified cloud video according to the video push plan. According to the application, the annotation information for the clip cloud video is acquired, the clip cloud video can be effectively annotated based on the annotation information, and the safety of subsequent cloud video push is improved.

The application discloses a video clip method, device, equipment and storage medium. The method comprises the following steps: an AI model identifies non-information segments, repeated segments and sensitive information segments in a target video uploaded by a user, labels the types of the segments on a time axis of the target video corresponding to the time periods of the segments, and generates default clip suggestions of the segment types; a video clip interface is displayed, the video clip interface displays a time axis, each time period along the time axis, and the types of the segments labeled on each time period, and the video clip interface further provides the default clip suggestions and a one-key processing button; and based on the operation of the one-key processing button by the user, the segments are processed according to the default clip suggestions, and a video after the clip is generated. The application can automatically clip the non-information segments, the repeated segments and the sensitive information segments in the target video synchronously, and the efficiency of the video clip is improved.

The present application relates to the technical field of biomedical image processing, and discloses a multi-scene video intelligent clipping method and system, comprising the following steps: obtaining time-synchronized original microscopic video stream and structured experimental operation log; for each operation event, constructing an event neighborhood reference window in the original microscopic video stream and performing spatial registration to generate a locally video segment after spatial correction; generating a spatial attention weight mask according to the operation space coordinate vector, calculating a structure disturbance response value sequence of each frame of the local video segment in combination with a structural similarity index, and obtaining a phase significance score sequence through smoothing filtering; generating an original candidate segment based on the discrete first-order derivative of the phase significance score sequence and baseline period statistical characteristics; according to the preset priority weight of the operation action type label corresponding to the candidate segment, performing cutting and transition processing on the time overlapping segment, and outputting an abstract video file; and generating a metadata index file associated with the abstract video file.

The application discloses a video key point migration method and system, relates to the technical field of multimedia data processing, and takes the clip trace file of the source video file clipped into a target video file as the basis for key point migration, breaks through the limitation of video key point migration through video matching, does not need to perform full decoding and feature extraction on the source video file and the target video file, only needs to realize the mapping of the video key point in the target video file according to the clip trace file, greatly reduces the computing power requirement, and even if the visual features of the video change greatly, the accurate migration of the video key point can be realized according to the clip trace file.

The application discloses a video clip method, device, equipment and storage medium, the method comprises the following steps: acquiring original video data for promoting a business object; taking a video scene as a cutting node, and cutting the original video data into a plurality of candidate clips; calculating a segment highlight value representing the highlight degree in vision for each candidate clip; selecting part of the candidate clips as target clips according to the segment highlight value; and splicing the target clips into target video data in sequence. In the case of getting rid of the subjective judgment of an art personnel, the candidate clips are cut according to the video scenes, the completeness of the plot is ensured, the segment highlight value is taken as a reference to screen the candidate clips, the quality of the screened candidate clips is ensured, the quality of the target video data is ensured, and the overall clip efficiency is improved.

The application discloses a video clip method, device and storage medium, relates to the technical field of video processing, and discloses a video clip method, which comprises the following steps: acquiring a video to be clipped and search text input by a user; dividing the video to be clipped into a plurality of candidate video clips according to preset clip configuration items; performing semantic matching on each candidate video clip based on the search text, to determine a target video clip matched with the search text; and generating a final clip video in response to preview editing operation of the user on the target video clip. The application accurately screens the target video clip through semantic matching, replaces manual screening of each segment, realizes adaptation of personalized requirements in combination with preview editing of the user, effectively improves video clip efficiency and accuracy, reduces the threshold of manual clipping, and balances the convenience and effect adaptability of clipping.

1. The name of the design product: storage box. 2. The use of the design product: the product is used for collecting images, RGB video materials, generating virtual human model intelligent video editing. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

This invention relates to the field of video production technology, specifically disclosing an intelligent video production method and system based on the Internet, comprising the following steps: S1: Obtaining user-inputted keywords, and calculating the matching degree based on keyword matching with material tags; S2: Obtaining the number of views and editing uses of the material, calculating the editing value, and filtering out materials that meet the requirements as reserve materials; S3: Dividing the reserve materials into material segments of preset duration, obtaining the number of bullet comments in the material segments and filtering out high-quality material segments, and calculating the popularity value of high-quality segments; S4: Calculating the propagation speed of high-quality segments, calculating the comprehensive editing value based on propagation speed and popularity value, sorting the reserve materials, and filtering out recommended materials. This invention improves the speed and accuracy of material retrieval through an intelligent method for filtering video materials, thereby increasing the video production rate and simplifying the video production process.

Aspects of the disclosure relate to systems and methods for setting advertisement breakpoints in a video, the system comprising a computer-readable storage medium storing a program. The program and method cause accessing a video; determining a plurality of shot boundaries of the video, each shot boundary defining a shot corresponding to a contiguous series of video frames without a cut or transition; and for each shot boundary of the plurality of shot boundaries, performing a set of breakpoint tests on the shot boundary, each breakpoint test configured to return a respective score indicating whether the shot boundary corresponds to a breakpoint for potentially inserting an advertisement during playback of the video, computing a combined score for the shot boundary based on combining each of the respective scores, and setting the shot boundary as a breakpoint if the combined score satisfies a threshold.

Embodiments of the present application provide a method, device and equipment for automatically clipping a video of interest, wherein the method for automatically clipping the video of interest comprises: obtaining a plurality of sets of records of potential frames of interest recorded synchronously by a video recording device when recording an original video; obtaining a plurality of potential video clips of interest based on the plurality of sets of records of potential frames of interest; reviewing each potential video clip of interest to determine one or more video clips of interest; and splicing the one or more video clips of interest into a video of interest. The method can filter and classify video frames synchronously during video recording to obtain corresponding potential video clips of interest, and the potential video clips of interest can be spliced into a video of interest after review. The method can improve recognition accuracy, reduce data processing amount of the system, save computing power, improve video clipping efficiency, and the review mechanism of the method can perform privacy processing on review data to maximize personal information protection and privacy compliance of users.

This application discloses a video editing method, apparatus, device, storage medium, and computer program product to address the problems of existing video editing solutions that rely heavily on manual operation, resulting in low editing efficiency, inability to handle the real-time editing needs of massive live videos, and highly subjective and inconsistent editing output. The method includes: sequentially extracting semantic features from each candidate video segment in an acquired set of candidate video segments to obtain a first semantic feature corresponding to each candidate video segment; determining a second semantic feature corresponding to the acquired editing requirements; selecting an editable video segment from the set of candidate video segments based on the feature similarity between the second semantic feature and the first semantic feature; and performing video editing based on the editable video segment.

Methods, systems, and computer storage media for providing a tailored meeting-video segment associated with a meeting-video management engine of a meeting-video management system. The tailored meeting-video segment corresponds to a portion of meeting-video content that is programmatically generated based on features associated with video data, meeting data, and user data. A tailored meeting-video segment—or a plurality of tailored meeting-video segments—can be generated by employing a meeting-video tailoring machine learning model of the meeting-video management engine. In particular, the features—associated with video data comprising the plurality of clips, meeting data of the meeting, and user data of the user—are meeting-video tailoring features used by the meeting-video tailoring machine learning model to generate the tailored meeting-video segment. The tailored meeting-video segment is communicated to a user to enable uniquely tailored presentation and playback of meeting-video content computed to be relevant to the user via the meeting-video management engine.

The application relates to the technical field of Internet of Things devices and discloses a video processing method, a storage medium and related devices, which comprises the following steps: receiving video data uploaded by a device end and marking each video data with a corresponding event mark; inputting each piece of video data and a preset prompt word into an AI large model to obtain a text description of each piece of video data; when a video summary request is received, performing deduplication on video data in a past preset time window according to the similarity between the text descriptions, so that the similarity between the text descriptions of any two pieces of video data in the deduplicated video data set is all lower than a preset similarity threshold; selecting a preset number of video data from the video data set as to-be-edited video data according to a preset summary rule; editing and splicing the to-be-edited video data into a summary video in chronological order, and pushing the summary video to a user terminal. The method can improve the information amount of the summary video and the acquisition efficiency of important monitoring information.

The application discloses a multi-camera language program synchronous sound intelligent editing method and system, the method comprises the following steps: step 1, multi-camera material preprocessing; step 2, multi-camera material content alignment on multiple tracks; step 3, script editing information extraction; step 4, automatic editing based on dialogue content; step 5, automatic shot switching based on speaker recognition; step 6, manual correction after completing intelligent editing. The system comprises a material preprocessing module, a multi-camera shot alignment module, a script analysis module, an automatic editing module, a speaker recognition module, a multi-shot switching module and an engineering editing module connected with each other. The application greatly reduces manual operation links, shortens editing time, improves editing precision and quality, effectively speeds up program production progress, and can adapt to personalized needs of different program types.

The application relates to a video generation method and device, computer equipment and a storage medium. The method comprises the following steps: subject classification is performed on video materials, thereby obtaining a plurality of subject events; each material segment in each subject event is scored, thereby obtaining quantized material scores; a plurality of material segments with high material scores are screened out in combination with video scene elements corresponding to a narrative style of the video materials; and a final video product is generated, without manual editing, and with different subject events logically arranged, so as to reflect the logical connection between different material segments, select material segments with high material scores in different subject events to generate a video product, improve the video quality of the video product, and solve the problem that existing video editing tools cannot understand the internal connection between materials, resulting in a lack of logic and story in the video product.

This invention relates to the field of video processing technology and discloses an intelligent video editing method based on big data. The method includes: obtaining visual boundary judgment values ​​based on brightness, motion abrupt changes, and cross-correlation decrease, and merging these values ​​with sentence-level temporal boundaries to form an anchor point sequence for video segmentation; inputting multimodal semantic representation and playback progress into a decision network to obtain highlight content scores, and selecting candidate highlight center frames within each video segment based on language prompt scores; calculating the total information and centroid of the preceding and following video layers to obtain the preceding semantic offset and establish asymmetric editing intervals; extracting the content representation of segments to construct a correlation graph, and solving for the segment path with the maximum coherence mean for temporal reassembly; finally mapping to the target timeline to generate a timestamp project file. This invention avoids highlight truncation errors, effectively preserves preceding context, and significantly improves the coherence of the final video.

This invention discloses a method and apparatus for generating interview transcript videos. The method includes: receiving interview videos and metadata uploaded by a personal camera; performing audio-video separation on the interview video to obtain an audio stream and a video stream; segmenting the audio stream, determining roles according to preset dimensions, and outputting structured text with role tags; the preset dimensions include at least one of speaking order, interrogative sentences, duration distribution, guiding words, and dispersion degree; segmenting the video stream and evaluating candidate clips from text, visual, and audio perspectives to obtain a ranking of the evaluated candidate clips; identifying user intent from video generation instructions; and generating an edited video based on the user intent, ambient lighting, the structured text, and the ranking of the candidate clips. This invention can automate the entire process of interview content acquisition, processing, and output, improving the production quality and efficiency of interview-related content.

The present disclosure provides a video processing method and device, electronic equipment and storage medium, belonging to the field of multimedia technology. The method comprises: in response to a video generation instruction for an article, displaying, in a video interface, a video generated based on a plurality of article images and article description text of the article, the plurality of article images and the article description text being generated by an artificial intelligence model based on a single reference image and detail information of the article, the plurality of article images being used to show the article from different angles or scenes, and the article description text being used to describe the characteristics of the article; and in response to a release instruction for the video, releasing the video. The above method not only can show the characteristics of the article in detail, but also solves the problem of lack of materials for the article, without the need for manual shooting of materials and editing of videos, effectively reducing the threshold for video generation, improving the video generation efficiency and release efficiency, and helping to improve the conversion rate of the article.

The present application relates to the field of video content generation, in particular to a video material clip automatic splicing method and system based on a large model, comprising: extracting the theme text of each segment in the video material; generating a first narrative text by fusing all theme texts; calculating the importance of each theme text therein; recording the theme text with the smallest importance as a class one text, and the rest as a class two text, and increasing the importance of the class one text based on the difference between the second narrative text generated based on the class two text and the first narrative text; extracting the common narrative features of all second narrative texts after repeating the process; dividing the theme texts into two categories according to the importance, so that the third narrative text fused by the first class theme text has the maximum similarity with the common narrative features; and splicing the corresponding video segments in the order of the first class theme text in the third narrative text. The present application automatically selects key segments from massive video materials and generates a video film that meets the user's demand.

This invention belongs to the field of computer vision and video understanding technology, and discloses a weakly supervised video temporal action localization method based on adaptive temporal continuity. The method includes acquiring an unedited video to be processed, extracting original segment features from the video, and generating an encoded feature sequence using a temporal encoder; performing parallel processing on the encoded feature sequence to generate class-independent action score sequences and class activation sequences, respectively; constructing a temporal continuity label generation module; constructing an instance dependency update module; and supervising the instance dependency update module using temporal continuity pseudo-labels to obtain the action localization result. This invention solves the technical problem in existing technologies where neglecting the temporal continuity of actions leads to severe instance confusion and boundary ambiguity in the model.