Patent Application
20250150660
The following disclosure is directed to systems and methods for editing product videos and, more specifically, providing automatic post-production editing of product videos.
Product videos are often used to showcase a particular product, highlighting its features, benefits, and use cases. These videos can be used for marketing purposes to promote and sell the product to potential customers. Product videos can take various forms, including live-action videos, animated videos, 3D renderings, and demonstrations. They can be created by companies, marketers, and/or individuals, and are often shared on social media, websites, and other online platforms to generate buzz and increase sales. The goal of a product video is to provide viewers with a clear understanding of what the product is, how it works, and why they should buy it. However, the post-production editing of such product videos can be a lengthy, manual process that varies based on product type. In many cases, the post-production editing process includes contributions from different individuals or teams.
At least one aspect of the present disclosure is directed to a method for providing automatic editing of a product video. The method includes receiving product video footage associated with a product, generating a feature list including a plurality of features of the product, assigning different portions of the product video footage to each feature of the plurality of features, creating a plurality of product artifacts from the portions of the product video footage assigned to each of the plurality of features, receiving a deliverable requirement list defining requirements for at least one media deliverable, and generating the at least one media deliverable using the plurality of product artifacts based on the deliverable requirement list.
In some embodiments, generating the feature list includes receiving product metadata data corresponding to the plurality of product features and generating the feature list based on the product metadata. In some embodiments, generating the feature list includes analyzing the product video footage to derive product metadata corresponding to the plurality of product features and generating the feature list based on the product metadata. In some embodiments, assigning different portions of the product video footage to each feature of the plurality of features includes assigning at least one timecode of the product video footage to each feature of the plurality of features.
In some embodiments, assigning different portions of the product video footage to each feature of the plurality of features includes assigning at least one video coordinate of the product video footage to each feature of the plurality of features. In some embodiments, the at least one video coordinate indicates a region of the product video footage where the corresponding feature is displayed. In some embodiments, the method includes assigning dimensions of a box to the at least one video coordinate. In some embodiments, the box encompasses a region of the product video footage where the corresponding feature is displayed.
In some embodiments, generating the at least one media deliverable using the plurality of product artifacts based on the deliverable requirement list includes integrating at least one infographic with at least one product artifact. In some embodiments, generating the at least one media deliverable using the plurality of product artifacts based on the deliverable requirement list includes integrating at least interactive element with at least one product artifact. In some embodiments, the at least one media deliverable includes at least one image. In some embodiments, the at least one media deliverable includes at least one video. In some embodiments, the at least one media deliverable includes at least one interactive video. In some embodiments, the at least one media deliverable includes two or more artifacts from the plurality of artifacts arranged in a sequence.
Another aspect of the present disclosure is directed to a system for automatically editing product video footage. The system includes at least one memory for storing computer-executable instructions and at least one processor for executing the instructions stored on the at least one memory. Execution of the instructions programs the at least one processor to perform operations that include receiving product video footage associated with a product, generating a feature list including a plurality of features of the product, assigning different portions of the product video footage to each feature of the plurality of features, creating a plurality of product artifacts from the portions of the product video footage assigned to each of the plurality of features, receiving a deliverable requirement list defining requirements for at least one media deliverable, and generating the at least one media deliverable using the plurality of product artifacts based on the deliverable requirement list.
In some embodiments, generating the feature list includes receiving product metadata data corresponding to the plurality of product features and generating the feature list based on the product metadata. In some embodiments, generating the feature list includes analyzing the product video footage to derive product metadata corresponding to the plurality of product features and generating the feature list based on the product metadata. In some embodiments, assigning different portions of the product video footage to each feature of the plurality of features includes assigning at least one timecode of the raw product video footage to each feature of the plurality of features.
In some embodiments, assigning different portions of the product video footage to each feature of the plurality of features includes assigning at least one video coordinate of the product video footage to each feature of the plurality of features. In some embodiments, the at least one video coordinate indicates a region of the product video footage where the corresponding feature is displayed. In some embodiments, execution of the instructions programs the at least one processor to perform operations that include assigning dimensions of a box to the at least one video coordinate. In some embodiments, the box encompasses a region of the product video footage where the corresponding feature is displayed.
In some embodiments, generating the at least one media deliverable using the plurality of product artifacts based on the deliverable requirement list includes integrating at least one infographic with at least one product artifact. In some embodiments, generating the at least one media deliverable using the plurality of product artifacts based on the deliverable requirement list includes integrating at least one interactive element with at least one product artifact. In some embodiments, the at least one media deliverable includes at least one image. In some embodiments, the at least one media deliverable includes at least one video. In some embodiments, the at least one media deliverable includes at least one interactive video. In some embodiments, the at least one media deliverable includes two or more artifacts from the plurality of artifacts arranged in a sequence.
Further aspects and advantages of the invention will become apparent from the following drawings, detailed description, and claims, all of which illustrate the principles of the invention, by way of example only.
A more complete appreciation of the invention and many attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings. In the drawings, like reference characters generally refer to the same parts throughout the different views. Further, the drawings are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the invention.
Disclosed herein are exemplary embodiments of systems and methods for editing product videos in post-production. In particular, described are various embodiments of a system configured to provide automatic post-production editing.
As discussed above, product videos are often used to showcase a particular product, highlighting its features, benefits, and use cases. These videos can be used for marketing purposes to promote and sell the product to potential customers. Product videos can take various forms, including live-action videos, animated videos, 3D renderings, and demonstrations. They can be created by companies, marketers, and/or individuals, and are often shared on social media, websites, and other online platforms to generate buzz and increase sales. The goal of a product video is to provide viewers with a clear understanding of what the product is, how it works, and why they should buy it.
Product videos can be filmed in a variety of ways, depending on the type of product, the desired outcome, and the budget. In many cases, product videos are filmed using a production plan that describes a list of shots that are desired for the video. Such production plans can include specific types of equipment and/or tools for capturing and editing the shots. In some examples, the filming of a product video includes several stages. For example, a pre-production stage includes developing a script, storyboard, or shot list (e.g., to be included in the production plan). The pre-production stage may also include selecting the right equipment, such as cameras, lighting, and sound equipment. During a set-up stage, the filming location is prepared with props, lighting, and any necessary equipment. The product may be set-up and positioned in a way that is visually appealing. Next, during a filming stage, the product is captured from different angles and distances (e.g., in accordance with the production plan). Depending on the type of product, close-ups of specific features or components may be included. In a post-production stage, the footage is edited to create the final product video. This may include adding music, voiceover, text overlays, and special effects. In some examples, color correction and audio editing may be performed. In a finalization stage, the product video is exported and/or compressed to an appropriate file format and resolution for a desired platform or medium.
The filming of a product video involves careful planning, attention to detail, and technical expertise. In some cases, the production plan that includes all of the shots needed to create the product video is created manually (e.g., by directors or producers of the product video). In other cases, the production plan for the product video may be generated automatically. Likewise, the product video footage may be recorded (or filmed) automatically based on the production plan. Examples of automatically generated production plans and automatically recorded product videos can be found in U.S. patent application Publication Ser. No. 18/331,494, filed Jun. 8, 2023 and titled “AUTOMATED PRODUCTION PLAN FOR PRODUCT VIDEOS,” which is hereby incorporated by reference in its entirety.
The editing of a product video (e.g., in the post-production stage) often includes specialized edits made by one or more video editors. As such, the editing of such product videos can be a lengthy, manual process. For example, the different editors and/or teams may provide unique contributions to the video editing process. Furthermore, discrepancies may arise between the product seller (or advertiser) and the editors when developing for product videos.
Accordingly, improved systems and methods for automatically editing product videos are provided herein. In at least one embodiment, raw product video footage associated with a product is used to generate a feature list including a plurality of features of the product. In some examples, different portions of the raw product video footage are assigned to each feature of the plurality of features and a plurality of product video artifacts are created from the assigned portions of the raw product video footage. In some examples, a deliverable requirement list defining requirements for at least one deliverable product video is used to generate a deliverable product video (or videos) using the plurality of video artifacts.
In some examples, the raw video footage 104 corresponds to video footage collected manually by a production team (e.g., via one or more cameras in a studio). In some examples, the raw video footage 104 corresponds to video footage collected automatically in an automated studio or facility. The use of an automated studio or facility may be beneficial in streamlining the post-production editing of the raw video footage 104. For example, using the same or similar shots (e.g., same lenses, camera movement, lights, background, etc.) for similar products may allow for streamlined post-production editing across different products. In some examples, the use of an automated studio enables the raw footage for similar products to be compared and/or combined (e.g., using seamless transitions). In some examples, the raw product footage 104 includes product metadata that is processed by the post-production engine 102 (described in greater detail below).
In addition to the raw video footage 104, the post-production engine 102 is configured to receive a requirement list 106 that defines (or incudes) requirements for one or more deliverables 108. In some examples, the requirement list 106 is provided or created by an end user of the deliverables 108. For example, the end user may be a seller, distributor, manufacturer, or advertiser of the product(s) associated with the raw video footage 104. In some examples, the post-production engine 102 is configured to use the requirement list 106 as a guideline (or reference) when editing the raw video footage 104 to produce the deliverables 108. The deliverables 108 may include videos and/or pictures. In some examples, the deliverables 108 are configured to be included in an interactive presentation (e.g., an interactive video or module). The deliverables 108 may be generated for marketing purposes, web pages, product detail pages (PDPs), printing, interactive videos, and any other form of audio-visual presentation.
In some examples, the post-production engine 102 is configured to communicate with one or more product databases 110 over a network 112 (e.g., a wired or wireless internet connection). In some examples, the post-production engine 102 receives (or retrieves) product data from the product database 110. The product data may correspond to the product(s) associated with the raw video footage 104. In some examples, the product database 110 may include data associated with products similar to (or related to) the product. For example, the product database 110 may include data previously collected from the same product. The product database 110 may include data previously collected from a different variation of the same product (e.g., a different color, size, etc.). In some examples, the product database 110 includes data associated with products made by the same manufacturer (e.g., the same brand). Likewise, the product database 110 may include data associated with similar products made by different manufacturers (e.g., different brands).
The post-production engine 102 is also configured to communicate with a resource library 114 (e.g., via network 112). In some examples, the resource library 114 includes graphical elements that may be incorporated with the raw video footage 104 to produce the deliverables 108. For example, such graphical elements may include logos, labels, signs, arrows, animations, infographics, and any other suitable type of graphic. It should be appreciated that the resource library 114 can include image-based and/or video-based graphics. In addition, the resource library 114 may include interactive elements that may be incorporated with the raw video footage 104 to produce the deliverables 108. For example, such interactive elements may include buttons, sliders, prompts, window boxes, drop down menus, and any other suitable type of interactive element.
As described in greater detail herein, the post-production plan engine 102 may utilize an artificial intelligence (AI) model 103 to generate (or assist with generating) the deliverables 108. In some examples, the AI model 103 is a generative pretrained transformer (GPT) model. In some examples, the AI model 103 may include other model types, such as, for example: a gradient boosted random forest, a regression, a neural network, a decision tree, a support vector machine, a Bayesian network, or other suitable types of techniques. In some examples, the AI model 103 is specifically trained for the purposes of video post-production.
At block 202, the raw video footage 104 associated with a product is received by the post-production engine 102. As described above, the raw video footage 104 may be provided manually (e.g., from a production team) or automatically (e.g., from an automated studio).
At block 204, product metadata for the product is obtained by the post-production engine 102. In some examples, the product metadata is received by the post-production engine 102 with the raw video footage 104. For example, the automated studio may construct and provide product metadata with the raw video footage 104. The product metadata includes information about representations of the product. Each product representation is a part of the product that has its own metadata and can be represented by audio/video assets. A product representation can be a feature, an element, an accessory, an action, or any important information about the product that can be presented visually. Examples of product representations of a stroller are: brakes, one hand folding, car seat, anti-vibrations, lightweight, and storage basket. Likewise, examples of product representations of a coffee maker are: water container, milk frother, espresso functionality. cappuccino functionality, cleaning, and type of capsules. Similarly, examples of product representations of a cookies box are: closed box, opened box, cookies types, a broken cookie, and list of ingredients.
In addition, the product metadata includes information about each shot in the raw video footage 104. For example, for each shot in the raw video footage 104, the product metadata may include: a shot ID, a product representation name, a product representation description, production notes (e.g., camera movement, type of shots, etc.), graphics to add (e.g., sizes, movement directions, etc.), text to add (e.g., features, modes, ingredients, safety, etc.), original audio included in the shot (e.g., toy squeaking, coffee maker operating, etc.), audio to add (e.g., music, narration, etc.), automated order given in production (e.g., to an automated robot, camera, etc.), or any combination thereof. In some examples, the post-production engine 102 is configured to generate a product feature list based on the received product metadata. The product feature list may include all features of the product. In some examples, the feature list includes physical features of the product, modes of the product, movements of the products, and actions or examples of the product in operation.
In cases where the product metadata is not provided with the raw video footage 104, the post-production engine 102 is configured to process and analyze the raw video footage 104 to extract the corresponding product metadata. In some examples, the post-production engine 102 is configured to retrieve product data from the product databases 110. For example, the retrieved product data may include digital product data (e.g., information from a PDP) and/or physical product data (e.g., images or scans of the product or product packaging). In some examples, the post-production engine 102 uses the product data as a guide (or reference) when analyzing the raw video footage 104. In some examples, the AI model 103 is used to assist in the extraction of the product metadata. For example, the AI model 103 may be trained to identify product representations in the raw video footage 104. In some examples, the retrieved product data from the product databases 110 is provided as an input to the AI model 103 along with the raw video footage 104. For each shot of the raw video footage 104, the post-production engine 102 may assign a shot ID, a product representation name, and a product representation description. In some examples, the product presentation description is generated via the AI model 103 based on the product representation name and/or the shot footage. In some examples, the post-production engine 102 is configured to generate a product feature list based on the generated product metadata and/or the product data retrieved from the product databases 110.
At block 206, the post-production engine 102 selects the best take from the raw video footage 104. For example, during production, several takes of the same area or feature of the product may be shot. In some examples, the post-production engine 102 analyzes these shots and selects the best take based on various video metrics (e.g., focus, smoothness, length, brightness, contrast, jitter, etc.). In some examples, the post-production engine 102 is configured to receive preferences from the end user and/or the production team that determine which video metrics to prioritize (e.g., via a user interface (UI) 116). In some examples, the post-production engine 102 is trained to weigh the various video metrics based on best production practice. If the raw video footage 104 is long, the post-production engine 102 may also cut the footage into smaller pieces and then analyze the quality of each piece. In such examples, the post-production engine 102 may select the takes (or pieces) with the best quality. It should be appreciated that the raw video footage 104 may include only one take, rendering this step optional or skippable. In some examples, the post-production engine 102 presents a plurality of video options to the end user and/or the production team. In such examples, the best take may be selected by the end user and/or the production team.
At block 208, the post-production engine 102 enables different portions of the raw video footage 104 to be assigned to features of the product. In some examples, each shot of the raw video footage is assigned to the features in the feature list generated from the product metadata. In some examples, the assignment (or mapping) of shots to features is based on input from a user (e.g., a production team member or the end user of the deliverables 108). For example, as shown in
In some examples, the post-production engine 102 is configured to derive product representation metadata from the connections made by the user.
In some examples, the assignment (or mapping) of shots to features is performed automatically (e.g., via the AI model 103). In other words, the product representation metadata 500 of
At step 1, a feature description 602 is provided for tagging a particular product feature in raw video footage 608. In some examples, the feature description 602 corresponds to a portion of the product metadata obtained (or received) in block 204. The feature description 602 may include information representing properties or characteristics of the associated product feature.
At step 2, the feature description 602 is provided to a trained neural network 604. The neural network 604 may be included in the AI model 103. In some examples, the neural network 604 is an external network in communication with the AI model 103 and/or other components of the post-production engine 102. In some examples, the neural network 604 is iteratively trained using historical product video datasets. For example, the neural network 604 may be trained using existing product video footage and corresponding information relating to the features included in the video footage (e.g., the locations of each feature). As such, the neural network 604 may be trained to automatically extract feature information from raw video footage.
At step 3, the trained neural network 604 is configured to output a feature vector 606 based on the feature description 602. In some examples, the feature vector 606 includes a plurality of numbers (e.g., rational numbers between −1 to 1) that represent one or more words associated with the feature description 602. For example, different vectors may be chosen for a library of different words. In some examples, each vector captures the semantic and syntactic qualities of the corresponding word(s). In some examples, the neural network 604 utilizes a natural language processing technique (e.g., Word2vec) to select (or generate) the feature vector 606.
At step 4, the raw video footage 608 is split into a plurality of frames 610. In some examples, the plurality of frames 610 includes a portion of the total frames in the raw video footage 608. For example, the plurality of frames 610 may include frames that appear at an interval (e.g., every 1 sec, every 2 secs, etc.).
At step 5, each frame of the plurality of frames 610 is provided to a trained neural network 612. In some examples, the neural network 612 may be the same as neural network 604; however, in other examples, the neural network 612 may be a different neural network (e.g., included in AI model 103 or an external network).
At step 6, the neural network 612 is configured to output a plurality of frame vectors 614 based on the plurality of frames 610 (e.g., one vector per frame). In some examples, each frame vector includes a plurality of numbers that represent one or more words associated with the frame (e.g., what is represented by the frame, what is featured in the frame, etc.). In some examples, the neural network 612 utilizes a natural language processing technique (e.g., Word2vec) to select (or generate) the frame vectors 614.
At step 7, the AI model 103 (or the post-production engine 102) compares each vector of the plurality of frame vectors 614 to the feature vector 606. The frame vector that is the closest match to the feature vector 606 is selected for the product feature associated with the feature description 602. In some examples, the “closest match” provides an indication of the frame having the best image of the product feature. In some examples, the AI model 103 is configured to locate the area within the selected frame that shows the center of action of the feature. In some examples, the AI model 103 is configured to select frames that are adjacent to the selected frame (e.g., ±3 secs, ±3 frames, etc.) to extract a video clip of the product feature from the raw video footage 608. It should be appreciated that instead of physically extracting video frames associated with the product feature, the AI model 103 may record the start and/or stop times associated with the identified video frames (e.g., similar to columns 508, 510 of product representation metadata 500 of
At step 1, a feature list 702 is provided for tagging specific product features in raw video footage 704. In some examples, the feature list 702 corresponds to a portion of the product metadata obtained (or received) in block 204. As described above, the product metadata may be used to generate the feature list 702. The feature list 702 may include information representing properties or characteristics of the associated product feature.
At step 2, each feature included in the feature list 702 is assigned to at least one object or action in the raw video footage 704. In some examples, the assignable objects/actions correspond to objects/actions that are known to be or expected to be included in the raw video footage 704. Each feature may be assigned to an object or action that represents or otherwise demonstrates the corresponding feature. For example, a “smooth driving” feature may be assigned to the wheels of a stroller, a “storage space” feature may be assigned to a basket of the stroller, and a “lightweight” feature may be assigned to the entire stroller, and a “one-hand folding” feature may be assigned to a folder version of the stroller. In some examples, the assignment of features to objects/actions is performed by the AI model 103. In some examples, the assignment of features to objects/actions is performed by a user (e.g., via the UI 116).
At step 3, the raw video footage 704 may be split into a plurality of frames 706. In some examples, the plurality of frames 706 includes a portion of the total frames in the raw video footage 704. For example, the plurality of frames 706 may include frames that appear at an interval (e.g., every 1 sec, every 2 secs, etc.).
At step 4, the plurality of frames 706 are searched to identify the objects/actions included in each frame. In some examples, the search is performed by the AI model 103. In some examples, the search is performed using an image search function (e.g., a program or function that performs an search using an image as the search query). The image search function may be an internal function (e.g., internal to the AI model 103 or the post-production engine 102) or an external function (e.g., that the AI model 103 or post-production engine 102 communicates with over network 112). In some examples, the results of the search are used to tag or label different objects/actions included in each frame. In some examples, the type of object/action and the location within the frame is recorded frame object/action.
At step 5, the AI model 103 (or the post-production engine 102) selects the best frame(s) for each feature in the feature list 702. In some examples, the “best” frame corresponds to the frame that provides the best image of the objects/actions assigned to the feature. For example, the frame(s) selected for a particular feature may correspond to the frame(s) where the object/actions assigned to the feature are largest and/or closest to the center of the frame. In some examples, the AI model 103 is configured to select frames that are adjacent to the selected frame(s) (e.g., ±3 secs, ±3 frames, etc.) to extract a video clip of the product feature from the raw video footage 704. It should be appreciated that instead of physically extracting video frames associated with the product features, the AI model 103 may record the start and/or stop times associated with the identified video frames (e.g., similar to columns 508, 510 of product representation metadata 500 of
It should be appreciated that the end user and/or the production team may review the feature assignments. In some examples, new features may be added and assigned manually by the end user and/or the production team.
Returning to
One editing job that may be performed is a clean footage job. The clean footage job finds unique parts in the raw video footage 104 that should be removed (e.g., unneeded text, logo, hands, etc.). In some examples, the user may provide a list of things to remove or keep. In such examples, the post-production engine 102 identifies those areas of the footage and cleans them automatically. Another editing job that may be performed is dynamic color correction. Based on the brightness of the filmed product, the post-production engine 102 dynamically applies color correction to get good looking, healthy-contrasted visuals. This also ensures color consistency across all product videos. Another editing job that may be performed is a trimming job. Based on the in/out values (e.g., columns 508 and 510 of product representation metadata 500 in
In some examples, the post-production engine 102 may perform a general video correction job. The general video correction job may automatically manage the size, speed, rotation, trims, and in/out times of all videos to ensure all videos have the same feel or appearance. Another editing job that may be performed is a change background job. The background in the raw video footage 104 (e.g., the production studio) may be automatically removed. In some examples, a desired background for the product is automatically added (e.g., a background supplied by the production team or the end user of the deliverables 108). For example, as shown in
In some examples, the post-production engine 102 is configured to provide samples to the end user and/or the production team before performing one or more of the editing jobs described above. For example, the post-production engine 102 may present a plurality of sample clips (or images) that represent different settings for an editing job (e.g., color correction). In such examples, the end user and/or production team may select the sample (or samples) that represent preferred settings. The post-production engine 102 may then perform the editing job using the preferred setting selected by the end user and/or the production team.
At block 212, the post-production engine 102 creates one or more artifacts from the portions of the raw video footage 104 (or the edited version(s) of the footage 104) assigned to the product features. Each artifact (or mini-artifact) is a fully edited building block that may be used to create the final deliverables 108. The artifacts may be still images, clean videos, videos with infographics, 360 degree videos, or any other suitable media building block. In some examples, the post-production engine 102 is configured to automatically create the artifacts using the produce representation metadata derived in block 208. The artifacts may be arranged and stored based on the product representations that they represent (e.g., in a database, folder structure, etc.). In some examples, the artifacts are stored with the product representation metadata that was used to create them. Such metadata may be made visible to users (e.g., via UI 116) to review the accuracy of the post-production engine 102. In some examples, the post-production engine 102 is configured to assign an ID to each artifact such that one or more of the artifacts can be retrieved for use in the final deliverables. In some examples, the post-production engine 102 is configured to present the artifacts to the end user and/or the production team. In such examples, the end user and/or the production team may provide feedback that is used by the post-production engine 102 to revise, alter, or modify the artifacts.
At block 214, the post-production engine 102 receives the requirement list 106. As described above, the requirement list 106 defines (or incudes) requirements for the deliverables 108. The requirement list 106 may include the quantity and/or the types of deliverables to create (e.g., images, videos, etc.). In some examples, the requirement list 106 may list specific features of the product to create deliverables for. The requirement list 106 may include rules for the deliverables 108. For example, such rules may include: a maximum video time, a minimum video time, a minimum number of features includes in a single deliverable, a maximum number of features includes in a single deliverable, types of features that should be included in the same deliverables, types of features that should be included in separate deliverables, etc. In some examples, the requirement list 106 includes a list of deliverables to be created. The list of deliverables may include an assignment of one or more product features to each deliverable. In some examples, the assignment of product features to deliverables may be performed automatically by the post-production engine 102. For example, the requirement list may include a deliverable that calls for three different features and the post-production engine 102 (or AI model 103) may assign three features having artifacts compatible with the deliverable type. In some examples, the post-production engine 102 may consider other factors (e.g., the rules described above) when assigning features (or artifacts) to each deliverable included in the requirement list 106.
At block 218, the post-production engine 102 integrates graphical elements and/or interactive elements with the artifacts. In some examples, the integration of the graphical and interactive elements is performed based on the requirement list 106. In some examples, the post-production engine 102 is configured to use the feature list 402 and the product representation metadata derived in step 208 when integrating the graphical and interactive elements with the artifacts. For example, when integrating an infographic into an artifact, the relevant text and information needed for the product representation may be pulled from the product representation metadata (or the product metadata). Likewise, the location of the infographic may be based on the location of the product representation, which may be pulled from the product representation metadata. In a similar manner, the post-production engine 102 may add labels, dimensions, arrows, technical details, animation, and other graphics using the product metadata and the product representation metadata.
In some examples, the post-production engine 102 can add both static and moving infographics to the video artifacts.
In some examples, the post-production engine 102 may utilize knowledge of the camera movement to integrate moving graphics relative to movement in the artifacts. For example,
In some examples, the post-production engine 102 adds interactive elements to the artifacts to create interactive artifacts. In some examples, the post-production engine 102 creates interactive branched videos (or video trees). In some examples, the post-production engine 102 is configured to create interactive branched videos using predetermined templates. For example, the post-production engine 102 may select a template based on the type of product(s) being featured. The template may include a storyboard or sequence for the artifacts to be arranged based on the types of features that they represent. In some examples, the post-production engine 102 uses an interactive video template that includes predefined areas (or locations) for the interactive elements. The interactive elements may be added to an interactive layer of the artifacts. In some examples, the post-production engine 102 is configured to add the interactive elements based on the requirement list 106, the custom branding elements, the product metadata, the product representation metadata, or any combination thereof.
In some examples, the post-production engine 102 is configured to present artifacts with integrated elements to the end user and/or the production team. In such examples, the end user and/or the production team may provide feedback that is used by the post-production engine 102 to revise, alter, or modify the elements and/or the artifacts. In some examples, the post-production engine 102 is configured to provide samples of the integrated artifacts to the end user and/or the production team. For example, the post-production engine 102 may present a plurality of sample artifacts that represent different variations of elements (e.g., different colors, styles, placement, etc.). In such examples, the end user and/or production team may select the sample (or samples) that represent preferred variations.
At block 220, the post-production engine 102 produces the final deliverables 108. The deliverables 108 are the final assets provided to the end user (e.g., the supplier of the requirement list 106). Each deliverable is created from one or more artifacts. As described above, the artifacts can include graphical elements and/or interactive elements. The deliverables can be pictures, videos, and/or interactive modules. In some examples, the final deliverables 108 include multiple deliverables of different media types (e.g., one picture and one video, one picture and two videos, etc.). The final deliverables 108 may be used for marketing purposes, web pages, PDPs, printing, interactive videos, and any other form of audio-visual presentation. Some examples of deliverables include: a video including all the artifacts that correspond to a product feature, a video showing different modes of a product feature (e.g., pouring espresso, americano, and cappuccino) all side-by-side in a video frame, an interactive video showing a 360 degree view of a product where a second video of a particular feature is displayed by clicking on the feature, a video showing all accessories for a product, a video advertisement demonstrating three product features where each feature has two individual videos, a collection of images or videos that have been formatted for different use cases (e.g., mobile vs. desktop, portrait vs. landscape, etc.), and an unboxing video that starts with a 360 degree view of the product packaging before transitioning to a 360 degree view of the unboxed product.
In some examples, each deliverable type corresponds to a deliverable template that is referenced in the requirement list 106.
As shown in
It should be appreciated that the video editing system 100 may be re-run using the same raw video footage 104 or the same requirement list 106. For example, a new requirement list 106 may be provided to create deliverables (and artifacts) for the same raw video footage 104. Likewise, new raw video footage 104 may be provided with the same requirement list 106 to create similar deliverables (and artifacts) for different products/brands. In one example, if the end user wants to focus on a product feature that was captured in the raw video footage 104 but wasn't defined as a feature before, the user can add the feature to the feature list (e.g., feature list 402 of
The processor 1902 can execute instructions within the computing device 1900, including instructions stored in the memory 1904. The processor 1902 may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor 1902 may provide, for example, for coordination of the other components of the device 1900, such as control of user interfaces, applications run by device 1900, and wireless communication by device 1900.
Processor 1902 may communicate with a user through control interface 1912 and display interface 1914 coupled to a display 1906. The display 1906 may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 1914 may comprise appropriate circuitry for driving the display 1906 to present graphical and other information to a user. The control interface 1912 may receive commands from a user and convert them for submission to the processor 1902. In addition, an external interface 1916 may be provided in communication with processor 1902, so as to enable near area communication of device 1900 with other devices. External interface 1916 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.
The memory 1904 stores information within the computing device 1900. The memory 1904 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. Expansion memory 1918 may also be provided and connected to device 1900 through expansion interface 1920, which may include, for example, a SIMM (Single In Line Memory Module) card interface. Such expansion memory 1918 may provide extra storage space for device 1900, or may also store applications or other information for device 1900. Specifically, expansion memory 1918 may include instructions to carry out or supplement the processes described above, and may include secure information also. Thus, for example, expansion memory 1918 may be provided as a security module for device 1900, and may be programmed with instructions that permit secure use of device 1900. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.
The memory may include, for example, flash memory and/or NVRAM memory, as discussed below. In one implementation, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer-or machine-readable medium, such as the memory 1904, expansion memory 1918, memory on processor 1902, or a propagated signal that may be received, for example, over transceiver 1410 or external interface 1916.
Device 1900 may communicate wirelessly through communication interface 1908, which may include digital signal processing circuitry where necessary. Communication interface 1908 may in some cases be a cellular modem. Communication interface 1908 may provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Such communication may occur, for example, through radio-frequency transceiver 1910. In addition, short-range communication may occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module 1922 may provide additional navigation-and location-related wireless data to device 1900, which may be used as appropriate by applications running on device 1900.
Device 1900 may also communicate audibly using audio codec 1924, which may receive spoken information from a user and convert it to usable digital information. Audio codec 1924 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 1900. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on device 1900. In some examples, the device 1900 includes a microphone to collect audio (e.g., speech) from a user. Likewise, the device 1900 may include an input to receive a connection from an external microphone.
The computing device 1900 may be implemented in a number of different forms, as shown in
Some implementations of the subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Implementations of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially-generated propagated signal. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).
The operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.
The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing. The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.
A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language resource), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, implementations of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending resources to and receiving resources from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.
Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some implementations, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server.
A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular implementations of particular inventions. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Thus, particular implementations of the subject matter have been described. Other implementations are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.
