Adobe, Inc.

Sourabh Gupta

Ajay Bedi

Saurabh Gupta

Gourav Singhal

Mrinal Kumar Sharma

Tushar Rajvanshi

Angad Kumar Gupta

Vikas Kumar

Nandan Jha

Jose Ignacio Echevarria Vallespi

Sankalp Shukla

Nirmal Kumawat

Saikat Chakrabarty

Shikhar Garg

Piyush Singh

Nishchey Arya

Rachit Gupta

Vijay Kumar Sharma

Tridib Das

Pooja

Srijan Sandilya

The present disclosure relates to systems, non-transitory computer-readable media, and methods for removing objects from an image stream at capture time of a digital image. For example, the disclosed system contemporaneously detects and segments objects from a digital image stream being previewed in a camera viewfinder graphical user interface of a client device. The disclosed system removes selected objects from the image stream and fills a hole left by the removed object with a content aware fill. Moreover, the disclosed system displays the image stream with the removed object and content fill as the image stream is previewed by a user prior to capturing a digital image from the image stream.

Removing objects at image capture time

A computer readable medium for sizing a product includes instructions, that when executed by at least one processor, cause a computing device to: retrieve from a webpage information on a product including product dimensions; present on a display of a client device a graphical button that upon access by a user activates a camera for capturing an image of an object positioned at a focal distance from the camera, the object having a surface; prompt the user to enter boundary information of an imaginary housing to be placed on the surface; generate the imaginary housing dimensions in two dimensions (2D) based on the boundary information and the focal distance; and determine whether the product fits within the imaginary housing by comparing the product dimensions against the imaginary housing dimensions.

Assisting users in visualizing dimensions of a product

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and providing pose guides that are tailored to a context of a camera viewfinder stream. In particular, in one or more embodiments, the disclosed systems determine a context of the camera viewfinder stream and provide sample pose images that correspond with the determined context. In response to a selection of a sample pose image, the disclosed systems generate and display a pose guide that is customized to the proportions of a subject depicted in the camera viewfinder stream. The disclosed systems further iteratively modify portions of the generated pose guide to indicate that the subject depicted in the camera viewfinder stream is coming into alignment with the generated pose guide. The disclosed systems automatically capture a digital image when the subject is in total alignment with the generated pose guide.

Providing contextual augmented reality photo pose recommendations

In some embodiments, techniques for producing user-generated content are provided. For example, a process may involve sending a product identifier; receiving a first candidate image that is associated with the product identifier; determining that a similarity between a user structure and a target structure satisfies a threshold condition, wherein the user structure characterizes a figure of a user in a first input image and the target structure is based on a pose guide associated with the first candidate image; and capturing, based on the determining, the first input image.

Pose recommendation and real-time guidance for user-generated content

An image generation system generates images of objects under different lighting conditions. An image of an object and lighting conditions for an output image are received. The lighting conditions may specify, for instance, a location and/or color of one or more light sources. The image of the object is decomposed into a shading component and a reflectance component. A machine learning model takes the reflectance component and specified lighting conditions as input, and generates an output image of the object under the specified lighting conditions. In some configurations, the machine learning model may be trained on images of objects labeled with object classes, and the output image may be generated by also providing an object class of the object in the image as input to the machine learning model.

Generating object images with different lighting conditions

Digital image ordering based on object position and aesthetics is leveraged in a digital medium environment. According to various implementations, an image analysis system is implemented to identify visual objects in digital images and determine aesthetics attributes of the digital images. The digital images can then be arranged in way that prioritizes digital images that include relevant visual objects and that exhibit optimum visual aesthetics.

Digital image ordering using object position and aesthetics

Techniques are provided herein for retrieving images that correspond to a target subject matter within a target context. Although useful in a number of applications, the techniques provided herein are particularly useful in contextual product association and visualization. A method is provided to apply product images to a neural network. The neural network is configured to classify the products in the images. The images are associated with a context representing the combination of classified products in the images. These techniques leverage both seller-provided images of products and user-generated content, which potentially includes hundreds or thousands of images of the same or similar products as the seller-provided images. A graphical user interface is configured to permit a user to select the context of interest in which to visualize the products.

Retrieving images that correspond to a target subject matter within a target context

Techniques are disclosed for identifying and presenting video content that demonstrates features of a target product. The video content can be accessed, for example, from a media database of user-generated videos that demonstrate one or more features of the target product so that a user can see and hear the product in operation via a product webpage before making a purchasing decision. The product functioning videos supplement any static images of the target product and the textual product description to provide the user with additional context for each of the product's features, depending on the textual product description. The user can quickly and easily interact with the product webpage to access and playback the product functioning video to see and/or hear the product in operation.

Integrating video content into online product listings to demonstrate product features

Review videos on product pages are enhanced with user interface elements that cause playback of the review videos at points at which particular topics are discussed. A review video is processed by converting speech to text and timestamping the text. Topics are identified in the text, and a time in the review video corresponding to each topic is identified from the timestamps for the text. In some configurations, sentiment is also determined for each topic. User interface elements corresponding to each identified topic are presented with the review video on the product page. When a user viewing the product page selects a user interface element, the review video is played at a time corresponding to the topic of the selected user interface element.

Enhancing review videos

Methods, systems, and non-transitory computer readable media are disclosed for determining a sub-set of user-submitted images that are similar to a curated image and presenting the sub-set of user-submitted images in connection with the curated image. The disclosed system presents a curated image depicting a product via a graphical user interface (e.g., on an e-commerce platform). In one or more embodiments, the disclosed system extracts feature vectors from the curated image and a plurality of user-submitted images. The disclosed system compares the feature vectors from the curated image and the plurality of user-submitted images to determine a sub-set of user-submitted images that are similar to the curated image. The disclosed system presents the sub-set of user-submitted images based on a user selection of the curated image.

Automatic clustering and mapping of user generated content with curated content

Described herein is a system and techniques for classification of subjects within image information. In some embodiments, a set of subjects may be identified within image data obtained at two different points in time. For each of the subjects in the set of subjects, facial landmark relationships may be assessed at the two different points in time to determine a difference in facial expression. That difference may be compared to a threshold value. Additionally, contours of each of the subjects in the set of subjects may be assessed at the two different points in time to determine a difference in body position. That difference may be compared to a different threshold value. Each of the subjects in the set of subjects may then be classified based on the comparison between the differences and the threshold values.

Classification of subjects within a digital image

Techniques are provided for providing a user with retrieved images of a specific article of clothing or accessory worn by models having a particular body type and pose as selected by the user. The images have been analyzed to identify both a body type and pose of the model wearing the article of clothing in each of the images. The images are labeled based on at least body type and pose of the model and clustered based on their labels, and are thus available for retrieval by subsequent requests by a user. In particular, a user that is interested in the article of clothing can input one or more requests for images of models having a selected body type and pose. Any of the images labeled with the selected body type and pose can then be provided to the user in any number ways.

Retrieving images that correspond to a target body type and pose

Methods, systems, and non-transitory computer readable media are disclosed for automatically, accurately, and efficiently correcting eye region artifacts including dark eye regions and wrinkles within a digital image portraying a human face. In particular, in one or more embodiments the disclosed systems localize areas within a digital image to identify eye region artifacts including dark eye regions and wrinkles. In one or more embodiments, the disclosed systems generate a corrected color image by correcting dark eye regions in a low frequency layer of the digital image by replacing the dark eye regions with candidate eye regions. Furthermore, in one or more embodiments the disclosed systems generate a corrected texture image by correcting wrinkles in a high frequency layer by processing the digital image utilizing a smoothing algorithm. The disclosed systems further generate a corrected digital image by combining the corrected color image and the corrected texture image.

Automatically correcting eye region artifacts in digital images portraying faces

In implementations of image content snapping guidelines, a guidelines segmentation system includes modules, such as an image pre-processing module to reduce the image size of a digital image if the image size exceeds an image size threshold. An object segmentation module segments objects depicted in the digital image and identifies each object by a bounding border that delineates an object region boundary. An edge detection module receives a segmented object and determines object external edges and object feature edges from the segmented object, and identifies object corners of the object. A snapping guidelines module determines image content snapping guidelines of an object depicted in the digital image, the image content snapping guidelines for an object determined based on the bounding border of the object region boundary, the object external edges, the object feature edges, and projected snapping guidelines that extend from the object corners of the object.

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Noida, India

Sourabh Gupta