WO2021120685A1 - Procédé et appareil de génération d'une vidéo et système informatique - Google Patents

Procédé et appareil de génération d'une vidéo et système informatique Download PDF

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Publication number
WO2021120685A1
WO2021120685A1 PCT/CN2020/111945 CN2020111945W WO2021120685A1 WO 2021120685 A1 WO2021120685 A1 WO 2021120685A1 CN 2020111945 W CN2020111945 W CN 2020111945W WO 2021120685 A1 WO2021120685 A1 WO 2021120685A1
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Prior art keywords
video
preset
target
initial
classification
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PCT/CN2020/111945
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English (en)
Chinese (zh)
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殷俊
赵筠
李勇
任宇
于思远
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苏宁云计算有限公司
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Priority to CA3164771A priority Critical patent/CA3164771A1/fr
Publication of WO2021120685A1 publication Critical patent/WO2021120685A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—3D [Three Dimensional] image rendering
    • G06T15/10—Geometric effects
    • G06T15/20—Perspective computation
    • G06T15/205—Image-based rendering

Definitions

  • the present invention relates to the field of computer vision technology, and in particular to a method, device and computer system for generating video.
  • the method of image-text video conversion is to cut out the product display map provided by the merchant, and then lay it out into the preset image background to form the product image, and obtain the video template, background music, etc. from the existing video material library in the platform Template files, based on these template files to generate product videos in batches.
  • the style and format of the commodity videos are completely dependent on the pre-configured template files in the material library, resulting in the generated videos with close styles and few layouts, and the actual status of the commodities cannot be visually presented to Consumers have limited expressive power.
  • the main purpose of the present invention is to provide a method for generating a video, so as to automatically generate a target video based on the initial video.
  • the present invention provides a video generation method, the method includes:
  • splicing the video segments corresponding to the target video classification to obtain the target video According to preset splicing parameters, splicing the video segments corresponding to the target video classification to obtain the target video.
  • the segmenting the initial video into video segments according to a preset video segmentation method includes:
  • the initial video is divided into video segments.
  • the shot boundary includes a sudden change shot and a gradual shot of the initial video, and dividing the initial video into video segments according to the determined shot boundary includes:
  • the mutation shot and the gradual change shot are removed from the initial video to obtain a set of video clips, and the set of video clips is composed of the video clips remaining after the removal.
  • the video is composed of continuous frames
  • the process of determining the mutation shot and the gradual shot includes:
  • the potential gradient frames are gradient frames, and the gradient lens is composed of the continuous gradient frames.
  • the inputting the video clips into a preset model, and determining the confidence of each of the video clips corresponding to all preset video classifications includes:
  • Preprocessing the sampling frame inputting the preprocessed sampling frame into the preset model, and obtaining the confidence level of the video segment corresponding to all the preset video classifications.
  • the inputting the preprocessed sampling frame into the preset model includes:
  • the spatio-temporal features included in the sample frame after preprocessing are extracted, and the spatio-temporal features are input into the preset model.
  • the preset model is a pre-trained MFnet three-dimensional convolutional neural network model.
  • the method further includes receiving a target duration, and determining the target video category corresponding to the target video category according to the target video category and the confidence of each of the video segments corresponding to all preset video categories.
  • Video clips include:
  • the video segment corresponding to the target video category is determined according to the target duration, the target video category, the confidence of each of the video segments corresponding to all preset video categories, and the duration of the video segment.
  • a video generation device the device includes:
  • Receiving module used to receive initial video and target video classification
  • a segmentation module configured to segment the initial video into video segments according to a preset video segmentation method
  • a processing module configured to input the video clips into a preset model, and determine the confidence level of each of the video clips corresponding to all preset video classifications;
  • a matching module configured to determine the video fragment corresponding to the target video classification according to the target video classification and the confidence of each of the video fragments corresponding to all preset video classifications;
  • the splicing module is used for splicing the video clips corresponding to the target video classification according to preset splicing parameters to obtain the target video.
  • this application provides a computer system, which includes:
  • One or more processors are One or more processors;
  • a memory associated with the one or more processors where the memory is used to store program instructions, and when the program instructions are read and executed by the one or more processors, perform the following operations:
  • splicing the video segments corresponding to the target video classification to obtain the target video According to preset splicing parameters, splicing the video segments corresponding to the target video classification to obtain the target video.
  • the present invention discloses a video generation method.
  • the initial video is divided into video segments, and the video segments are input into a preset model, Obtain the confidence of each of the video segments corresponding to all preset video categories According to the target video category and the confidence of each of the video segments corresponding to all the preset video categories, determine the video corresponding to the target video category Fragments; according to preset splicing parameters, the video fragments corresponding to the target video classification are spliced to obtain the target video, which realizes the generation of the target video that meets the requirements according to the initial video, and ensures the timeliness and accuracy of video generation ;
  • the present invention also proposes using a preset shot boundary detection method to determine the shot boundary included in the initial video; according to the determined shot boundary, the initial video is divided into video segments, and further proposes the The shot boundary includes a sudden change shot and a gradual change shot of the initial video.
  • the segmenting the initial video into video segments according to the determined shot boundary includes: dividing the sudden change shot and the gradual change shot from the initial The video is eliminated to obtain a set of video segments, and the set of video segments is composed of the remaining video segments after the elimination. Ensure the accuracy of video segmentation;
  • This application discloses sampling the video clip according to a preset sampling method to obtain at least two sampling frames corresponding to the video clip; preprocessing the sampling frame, and inputting the preprocessed sampling frame
  • the preset model obtains the confidence levels of all preset video categories corresponding to the video clip; determines that the preset video category corresponding to the confidence level with the largest value is the preset video category corresponding to the video clip Set the video classification, the confidence with the largest value is the confidence of the video segment; according to the preset video classification and the confidence corresponding to all the video fragments, determine the corresponding to the target video classification
  • the confidence of the video segment and the corresponding video segment ensures the accuracy of the calculation of the confidence.
  • Fig. 1 is a schematic diagram of a model network structure provided by an embodiment of the present application.
  • FIG. 2 is a flowchart of lens segmentation provided by an embodiment of the present application.
  • Fig. 3 is a flow chart of model training provided by an embodiment of the present application.
  • Figure 4 is a flowchart of a method provided by an embodiment of the present application.
  • FIG. 5 is a structural diagram of an apparatus provided by an embodiment of the present application.
  • Fig. 6 is a structural diagram of a computer system provided by an embodiment of the present application.
  • the two commonly used methods for generating commercial videos in the prior art each have certain limitations.
  • the manual editing method requires high labor costs and low efficiency, and cannot meet the actual needs of generating large-scale commodity videos; the video generation method based on image-text conversion is more efficient, but there are fewer video formats and video styles available. Fixed, limited expression ability.
  • this application proposes to segment the videos uploaded by users using a preset segmentation method to obtain video segments, use a preset classification model to classify each video segment, and obtain each video The confidence level corresponding to the segment; according to the target video classification selected by the user, the video segment in the classification whose confidence level meets the preset condition is spliced to obtain the target video. It is realized that the target video that meets the requirements is generated according to the video uploaded by the user, while ensuring the timeliness of the video generation.
  • the classification model In order to realize the classification of the video clips obtained by segmentation, the classification model needs to be trained in advance.
  • the MFnet three-dimensional convolutional neural network model can be used as the classification model.
  • the MFnet three-dimensional convolutional neural network model is a lightweight deep learning model. Compared with the recent deep learning models such as I3D and SlowFastnet, its model is more streamlined, the amount of floating-point operations is less, and it is on the test data set. The test effect is better.
  • the training process includes:
  • the training data set can be generated by the following methods:
  • the categories include but are not limited to the appearance of the main body of the product, the use scene of the product, and the introduction of the product content, and manually edit according to the divided categories.
  • N represents the number of sampling frames for each sub-video clip folder
  • C represents each sub-video clip folder.
  • H represents the preset height of each frame
  • W represents the preset width of each frame.
  • N is at least 8.
  • Figure 1 shows a schematic diagram of the network structure of the model, including 3DCNN, which is used to extract the three-dimensional convolutional features contained in each sample.
  • the three-dimensional convolutional features include temporal and spatial features, including the movement trend of commodities, changes in background, and other video streams. Movement information of the inner object.
  • 3Dpooling is the pooling layer of the model, used to pool the output of 3DCNN, and input the pooling result into the 3D MF-Unit layer for 1 ⁇ 1 ⁇ 1, 3 ⁇ 3 ⁇ 3, 1 ⁇ 3 ⁇ 3, etc.
  • Different convolution operations
  • Global Pool is the global pool layer, used to retain the main characteristics of the input results while reducing unnecessary parameters
  • FClayer is a fully connected layer, used to output the confidence of each video segment corresponding to each category.
  • the model can classify samples obtained by intensive sampling of a single lens.
  • the classification accuracy rate reaches 95.92%
  • the single model is only 29.6MB, which is aimed at a single lens.
  • the forward inference time of densely sampled video is 330ms, with high accuracy and fast speed.
  • the video can be generated according to the model. As shown in Figure 2, the generation process includes:
  • Step 1 Receive the initial video input by the user
  • Step 2 Perform shot boundary detection on the initial video, segment the video according to the detection result, remove redundant segments, and obtain video segments;
  • the shot boundary detection process includes:
  • each frame of the initial video is divided into a preset number of sub-blocks using the same preset method, and then the sub-histogram of each sub-block is calculated.
  • the sub-histogram the sub-blocks at the same position in adjacent frames are calculated.
  • the histogram difference the adjacent frames of each frame include the previous frame and the next frame of the frame.
  • T H a first predetermined threshold
  • the difference indicates that the corresponding subblock is large between adjacent frames
  • the frame difference when a large number of sub-blocks is higher than a second preset threshold, i.e., that This frame is a sudden change frame, and the continuous sudden change frames constitute a sudden change shot.
  • Step 3 Sample the video clips, input the sampling results into a preset model, and obtain the category and confidence level corresponding to each video clip;
  • the above video clips are randomly and densely sampled.
  • the random dense sampling process includes:
  • Randomly initialize sampling points on the video segment take the sampling point as seven points, and focus on the end of the video segment, uniformly sample N frames, and preprocess the sample frames to meet the input size requirements of the preset model.
  • the preprocessed sample frames are input into a preset model, and the confidence levels of the video clips containing the sample frames corresponding to all categories are obtained.
  • Step 4 According to the target category and target duration selected by the user, stitch the video clips corresponding to the target category to generate the target video;
  • the video clips are sorted according to the confidence of the corresponding appearance display category, and video clips that meet the requirements are screened.
  • Specific screening rules can include:
  • the video segment with the highest confidence level is directly used as the target video;
  • the next n video segments T j are sequentially selected according to the order of the confidence value, where j ⁇ [1,n], until the following formula is satisfied :
  • T 2 -T 1 represents the target duration
  • the duration of the n+1 shots selected according to the confidence score exceeds the maximum duration T 2 , the longest shot among them will be intercepted head and tail according to the duration of each shot until the total duration meets the requirement of the target duration.
  • Step 5 The video clips obtained in step 4 are sequentially spliced according to the time sequence of the initial video to obtain the target video.
  • the generated target video it can be stored in a video database and reused when needed next time, or used to continue training the model.
  • this application provides a method for generating a video. As shown in FIG. 4, the method includes:
  • a preset video segmentation method segment the initial video into video segments.
  • the method includes:
  • the shot boundary includes a sudden change shot and a gradual shot of the initial video
  • the method includes:
  • the video is composed of continuous frames
  • the process of determining the mutation shot and the gradual shot includes:
  • the potential gradient frames are gradient frames, and the gradient lens is composed of the continuous gradient frames.
  • the method includes:
  • Preprocessing the sampling frame inputting the preprocessed sampling frame into the preset model, and obtaining the confidence level of the video segment corresponding to all the preset video classifications.
  • the obtained sampling frames are at least eight frames.
  • the inputting the preprocessed sampling frame into the preset model includes:
  • the method further includes receiving a target duration, and determining the video segment corresponding to the target video category according to the target video category and the confidence of each of the video segments corresponding to all preset video categories includes :
  • splicing the video segments corresponding to the target video classification to obtain a target video According to preset splicing parameters, splicing the video segments corresponding to the target video classification to obtain a target video.
  • this application provides a video generation device. As shown in FIG. 5, the device includes:
  • the receiving module 510 is used to receive initial video and target video classification
  • the segmentation module 520 is configured to segment the initial video into video segments according to a preset video segmentation method
  • the processing module 530 is configured to input the video clips into a preset model, and determine the confidence of each of the video clips corresponding to all preset video classifications;
  • the matching module 540 is configured to determine the video fragment corresponding to the target video classification according to the target video classification and the confidence of each of the video fragments corresponding to all preset video classifications;
  • the splicing module 550 is configured to splice the video clips corresponding to the target video classification according to preset splicing parameters to obtain the target video.
  • the segmentation module 520 may also be used to use a preset shot boundary detection method to determine the shot boundary included in the initial video;
  • the initial video is divided into video segments.
  • the shot boundary includes a sudden change shot and a gradual change shot of the initial video
  • the segmentation module 520 may also be used to remove the sudden change shot and the gradual change shot from the initial video to obtain a set of video clips
  • the set of video clips is composed of the video clips remaining after culling.
  • the video is composed of continuous frames
  • the segmentation module 520 may also be used to calculate the degree of difference between all the frames and adjacent frames of the frame; when the degree of difference exceeds a first preset Threshold, determine that the frame is a sudden change frame, and the sudden change shot is composed of continuous sudden change frames; when the degree of difference is between a first preset threshold and a second preset threshold, determine that the frame is Potential gradient frame; when the number of consecutive potential gradient frames exceeds a third preset threshold, it is determined that the potential gradient frame is a gradient frame, and the gradient lens is composed of the continuous gradient frames.
  • the matching module 530 can also be used to sample the video clip according to a preset sampling method to obtain at least two sampling frames corresponding to the video clip; The subsequent sampling frames are input to the preset model, and the confidence levels of the video segments corresponding to all the preset video classifications are obtained.
  • the matching module 530 may also be used to extract the spatiotemporal features contained in the sample frame after preprocessing, and input the spatiotemporal features into the preset model.
  • the preset model is a pre-trained MFnet three-dimensional convolutional neural network model.
  • the receiving module 510 can also be used to receive a target duration
  • the matching module 540 can also be used to receive the target duration, the target video classification, and the confidence level of each of the video segments corresponding to all preset video classifications. , The duration of the video segment, determining the video segment corresponding to the target video category.
  • the fourth embodiment of the present application provides a computer system, including: one or more processors; and a memory associated with the one or more processors, and the memory is used to store program instructions , When the program instructions are read and executed by the one or more processors, perform the following operations: receive initial video and target video classification;
  • splicing the video segments corresponding to the target video classification to obtain the target video According to preset splicing parameters, splicing the video segments corresponding to the target video classification to obtain the target video.
  • FIG. 6 exemplarily shows the architecture of the computer system, which may specifically include a processor 1510, a video display adapter 1511, a disk drive 1512, an input/output interface 1513, a network interface 1514, and a memory 1520.
  • the processor 1510, the video display adapter 1511, the disk drive 1512, the input/output interface 1513, the network interface 1514, and the memory 1520 may be communicatively connected through the communication bus 1530.
  • the processor 1510 can be implemented by a general CPU (Central Processing Unit, central processing unit), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc., for Perform relevant procedures to realize the technical solutions provided in this application.
  • a general CPU Central Processing Unit, central processing unit
  • a microprocessor e.g., a central processing unit
  • ASIC Application Specific Integrated Circuit
  • the processor 1510 can be implemented by a general CPU (Central Processing Unit, central processing unit), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc., for Perform relevant procedures to realize the technical solutions provided in this application.
  • ASIC Application Specific Integrated Circuit
  • the memory 1520 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory, random access memory), static storage device, dynamic storage device, etc.
  • the memory 1520 may store an operating system 1521 for controlling the operation of the computer system 1500, and a basic input output system (BIOS) for controlling the low-level operation of the computer system 1500.
  • BIOS basic input output system
  • a web browser 1523, a data storage management system 1524, and an icon font processing system 1525 can also be stored.
  • the foregoing icon font processing system 1525 may be an application program that specifically implements the foregoing steps in the embodiment of the present application.
  • the related program code is stored in the memory 1520 and is called and executed by the processor 1510.
  • the input/output interface 1513 is used to connect input/output modules to realize information input and output.
  • the input/output/module can be configured in the device as a component (not shown in the figure), or can be connected to the device to provide corresponding functions.
  • the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and an output device may include a display, a speaker, a vibrator, an indicator light, and the like.
  • the network interface 1514 is used to connect a communication module (not shown in the figure) to realize communication interaction between the device and other devices.
  • the communication module can realize communication through wired means (such as USB, network cable, etc.), or through wireless means (such as mobile network, WIFI, Bluetooth, etc.).
  • the bus 1530 includes a path to transmit information between various components of the device (for example, the processor 1510, the video display adapter 1511, the disk drive 1512, the input/output interface 1513, the network interface 1514, and the memory 1520).
  • various components of the device for example, the processor 1510, the video display adapter 1511, the disk drive 1512, the input/output interface 1513, the network interface 1514, and the memory 1520.
  • the computer system 1500 can also obtain information about specific receiving conditions from the virtual resource object receiving condition information database 1541 for condition judgment, and so on.
  • the above device only shows the processor 1510, the video display adapter 1511, the disk drive 1512, the input/output interface 1513, the network interface 1514, the memory 1520, the bus 1530, etc., in the specific implementation process, the The device may also include other components necessary for normal operation.
  • the above-mentioned device may also include only the components necessary to implement the solution of the present application, and not necessarily include all the components shown in the figure.

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Abstract

L'invention concerne un procédé et un appareil de génération d'une vidéo, ainsi qu'un système informatique (1500). Le procédé comprend les étapes consistant à : recevoir une vidéo initiale et une classification vidéo cible (410); segmenter la vidéo initiale en segments vidéo en fonction d'un procédé de segmentation vidéo prédéfini (420); entrer les segments vidéo dans un modèle prédéfini de façon à déterminer la fiabilité de chacun des segments vidéo correspondant à toutes les classifications vidéo prédéfinies (430); déterminer les segments vidéo correspondant à la classification vidéo cible en fonction de la classification vidéo cible et de la fiabilité de chacun des segments vidéo correspondant à toutes les classifications vidéo prédéfinies (440); et assembler les segments vidéo correspondant à la classification vidéo cible en fonction de paramètres d'assemblage prédéfinis de façon à obtenir une vidéo cible (450). Une vidéo cible qui répond aux exigences est générée automatiquement en fonction de la vidéo initiale, ce qui garantit la rapidité et la précision de la génération de la vidéo.
PCT/CN2020/111945 2019-12-20 2020-08-28 Procédé et appareil de génération d'une vidéo et système informatique WO2021120685A1 (fr)

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CN201911330586.7A CN111161392B (zh) 2019-12-20 2019-12-20 一种视频的生成方法、装置及计算机系统
CN201911330586.7 2019-12-20

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CN111935528B (zh) * 2020-06-22 2022-12-16 北京百度网讯科技有限公司 视频生成方法和装置
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CN111161392B (zh) 2022-12-16

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