Patent Publication Number: US-2021182950-A1

Title: System and method for transforming images of retail items

Description:
PRIORITY STATEMENT 
     The present application hereby claims priority to Indian patent application number 201941052026 filed on 16 Dec. 2019, the entire contents of which are hereby incorporated herein by reference. 
     BACKGROUND 
     Embodiments of the description generally relate to systems and methods for transforming images of retail items, and more particularly to systems and methods for transforming images of retail items using generative models. 
     On-line shopping (e-commerce) platforms for retail items are well known. Shopping for fashion items on-line is growing in popularity because it potentially offers users a broader range of choice of items in comparison to earlier off-line boutiques and superstores. 
     Typically, most fashion e-commerce platforms show catalogue images with human models wearing the fashion retail items. The models are shot in various poses and the photos are displayed on the e-commerce platforms. These photoshoots happen in studios and the background and other features of the images are selected according to the retail items and/or brand being shot. However, the process is time consuming and adds to the cost of cataloguing. Moreover, shoppers on e-commerce platforms may want to try out different fashion retail items on them before making an actual on-line purchase of the item. This will give them the experience of “virtual try-on”, which is not easily available on most e-commerce shopping platforms. 
     Thus, there is a need for systems and methods that enable faster and cost-effective cataloguing of retail items. Further, there is a need for systems and methods that enable the shoppers to virtually try-on the retail items. 
     SUMMARY 
     The following summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, example embodiments, and features described, further aspects, example embodiments, and features will become apparent by reference to the drawings and the following detailed description. 
     Briefly, according to an example embodiment, a system for transforming images of retail items is presented. The system includes an image acquisition unit configured to access an input image of a selected retail item and a sample target image. The system further includes a processor operatively coupled to the image acquisition unit. The processor includes a training module, a latent vector generator, a latent vector modifier, and an image generator. The training module is configured to train a generative model using a set of training input images and a set of training target images. The latent vector generator is configured to generate a first latent vector from the trained generative model based on the input image of the selected retail item, and to generate a second latent vector from the trained generative model based on the sample target image. The latent vector modifier is configured to modify the second latent vector based on the first latent vector to generate a modified latent vector; and the image generator is configured to generate an output image based on the modified latent vector. 
     According to another example embodiment, a system for transforming flat shot images of fashion retail items to catalogue images is presented. The system includes an image acquisition unit configured to receive a flat shot image of a selected fashion retail item and a sample catalogue image. The system further includes a processor operatively coupled to the image acquisition unit. The processor includes a training module, a latent vector generator, a latent vector modifier, and an image generator. The training module is configured to train a generative adversarial network using a set of training flat shot images and a set of training catalogue images. The latent vector generator is configured to generate a first latent vector from the trained generative adversarial network based on the flat shot image of the selected retail item, and to generate a second latent vector from the trained generative adversarial network based on the sample catalogue image. The latent vector modifier is configured to modify the second latent vector based on the first latent vector to generate a modified latent vector; and the image generator is configured to generate an output catalogue image based on the modified latent vector. 
     According to yet another example embodiment, a method for transforming images of retail items is presented. The method includes training a generative model using a set of training input images and a set of training target images. The method further includes presenting an input image of a selected retail item to the trained generative model to generate a first latent vector; and presenting a sample target image to the trained generative model to generate a second latent vector. The method furthermore includes modifying the second latent vector based on the first latent vector to generate a modified latent vector; and generating an output image based on the modified latent vector. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       These and other features, aspects, and advantages of the example embodiments will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
         FIG. 1  is a block diagram illustrating a system for transforming images of retail items, according to some aspects of the present description, 
         FIG. 2  is a flow chart illustrating a method for transforming images of retail items, according to some aspects of the present description, 
         FIG. 3  illustrates an example embodiment for generating a catalogue image of a dress from a flat shot image of the dress, according to some aspects of the present description, 
         FIG. 4  illustrates an example embodiment for generating a catalogue image of a dress from a flat shot image of the dress, according to some aspects of the present description, 
         FIG. 5  illustrates an example embodiment for generating a plurality of catalogue images with different model poses from a flat shot image of a dress, according to some aspects of the present description, 
         FIG. 6  illustrates an example embodiment for generating a plurality of catalogue images with different model poses and accessories from a flat shot image of a dress, according to some aspects of the present description, 
         FIG. 7  illustrates an example embodiment for generating a catalogue image of a hand bag from a flat shot image of the hand bag, according to some aspects of the present description, 
         FIG. 8  illustrates an example embodiment for generating a catalogue image of a dress from an image of a mannequin wearing the dress, according to some aspects of the present description, 
         FIG. 9  illustrates an example embodiment for generating an image of a shopper wearing a dress from a flat shot image of the dress, according to some aspects of the present description, and 
         FIG. 10  illustrates an example embodiment for generating a flat shot image of a dress from a catalogue image of the dress, according to some aspects of the present description. 
     
    
    
     DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS 
     Various example embodiments will now be described more fully with reference to the accompanying drawings in which only some example embodiments are shown. Specific structural and functional details disclosed herein are merely representative for purposes of describing example embodiments. Example embodiments, however, may be embodied in many alternate forms and should not be construed as limited to only the example embodiments set forth herein. 
     The drawings are to be regarded as being schematic representations and elements illustrated in the drawings are not necessarily shown to scale. Rather, the various elements are represented such that their function and general purpose become apparent to a person skilled in the art. Any connection or coupling between functional blocks, devices, components, or other physical or functional units shown in the drawings or described herein may also be implemented by an indirect connection or coupling. A coupling between components may also be established over a wireless connection. Functional blocks may be implemented in hardware, firmware, software, or a combination thereof. 
     Before discussing example embodiments in more detail, it is noted that some example embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations as sequential processes, many of the operations may be performed in parallel, concurrently or simultaneously. In addition, the order of operations may be re-arranged. The processes may be terminated when their operations are completed, but may also have additional steps not included in the figures. It should also be noted that in some alternative implementations, the functions/acts/steps noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed substantially concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved. 
     The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. As used herein, the singular forms “a,” “an,” and “the,” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the terms “and/or” and “at least one of” include any and all combinations of one or more of the associated listed items. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     Example embodiments of the present description present systems and methods for transforming images of retail items using generative models. 
       FIG. 1  is a block diagram of a system  100  for transforming images of retail items using generative models. The system  100  includes an image acquisition unit  102  and a processor  104  operatively coupled to the image acquisition unit  102 . The processor  104  further includes a training module  106 , a latent vector generator  108 , a latent vector modifier  110 , and an image generator  112 . The image acquisition unit  102  and the components of the processor  104  are described in further detail below. 
     The image acquisition unit  102  is configured to access an input image  10  of a selected retail item  12  and a sample target image  20 . The term “selected retail item” as used herein refers to a retail item whose image needs to be transformed by the systems and methods described herein. Non-limiting examples of retail items include fashion retail items, furniture items, decorative items, linen, furnishing (carpets, cushions, and curtains), lamps, tableware, and the like. In one embodiment, the selected retail item is a fashion retail item. Non-limiting examples of fashion retail items include garments (such as top wear, bottom wear, and the like), accessories (such as scarves, belts, socks, sunglasses, and bags), jewelry, foot wear and the like. 
     In one embodiment, the input image  10  of the selected retail item is captured in real time by a suitable imaging device (not shown). The imaging device may include a camera configured to capture visible, infrared, or ultraviolet light. The image acquisition unit  102  in such instances may be configured to access the imaging device and the input image  10  in real time. In another embodiment, the input image  10  of the selected retail item is stored in an input image repository (not shown) either locally (e.g., in a memory coupled to the processor  104 ) or in a remote location (e.g., cloud storage, offline image repository and the like). The image acquisition unit  102  in such instances may be configured to access the input image repository to retrieve the input image  10 . 
     The input image  10  may be a standalone image of the selected retail item  12  in one embodiment. The term “standalone image” as used herein refers to the image of the selected retail item by itself. In embodiments related to fashion retail items, the “standalone image” does not include a model or a mannequin. In certain embodiments, the input image  10  may be a flat shot image of the selected retail item. The flat shot images may be taken from any suitable angle and include top-views, side views, front-views, back-views, and the like. In another embodiment related to a fashion retail item, the input image  10  may be an image of a mannequin wearing the selected retail item  12 . The input images  10  as described herein are applicable to embodiments related to transformation of images (standalone or mannequin-based) to catalogue images or virtual try-on images. For embodiments related to transformation of catalogue images to standalone images of the retail items, the input image  10  is a catalogue image of the selected retail item. 
     In the example embodiment illustrated in  FIG. 1 , the selected retail item  12  is shown as a dress and the input image  10  as a flat shot image of the front view of the dress. However, as noted earlier, any retail item is within the scope of the present description. Further, the input image  10  may be a standalone image of the selected retail item taken from any suitable angle. Alternatively, in embodiments related to fashion retail items, the input image  10  could also be an image of a mannequin wearing the selected fashion retail item, as shown in  FIG. 8 . 
     With continued reference to  FIG. 1 , the image acquisition unit  102  is further configured to access a sample target image  20 . The term “sample target image” as used herein refers to an image having one or more characteristics that are desired in the image after transformation. For example, for retail items such as furniture items, the sample target image  20  may have the desired background required in the final output image. Similarly, for cataloguing of fashion retail items, the sample target image  20  may have the characteristics (e.g., model attributes, background etc) desired for the final catalogue image. Alternatively, for embodiments related to shoppers virtually trying on the selected retail items, the sample target image  20  may be an image of the shopper. In one embodiment, the sample target image  20  is an image of a model wearing another retail item. In another embodiment, the sample target image  20  is an image of a shopper wearing another retail item. 
     The sample target image  20  may be stored in a sample target image repository (not shown) either locally (e.g., in a memory coupled to the processor  104 ) or in a remote location (e.g., cloud storage, offline image repository and the like). The image acquisition unit  102  in such instances may be configured to access the sample target image repository to retrieve the sample target image  20 . Alternatively, for embodiments related to shoppers virtually trying on the selected retail items, the sample target image  20  may be provided by the shopper. In such instances, the image acquisition unit  102  may be configured to access the sample target image  20  from the user interface where the shopper has uploaded the sample target image  20 . 
     Referring back to  FIG. 1 , the processor  104  is communicatively coupled to the image acquisition unit  102 . The processor includes a training module  106  configured to train a generative model using a set of training input images  114  and a set of training target images  116 . The term “generative model” as used herein refers to a machine learning model that is able to replicate or generate new data instances. Non-limiting examples of suitable generative models include a Generative Adversarial Network, a cycle Generative Adversarial Network, or a bidirectional Generative Adversarial Network. In one embodiment, the generative model is a Generative Adversarial Network (GAN). 
     The processor  104  further includes a latent vector generator  108  that is communicatively coupled to the image acquisition unit  102  and the training module  106 . The latent vector generator  108  is configured to receive the input image  10  and the sample target image  20  from the image acquisition unit  102 . The latent vector generator  108  is further configured to receive the trained generative model  118  from the training module  106 , and present the input image  10  and the sample target image  20  to the trained generative model. The latent vector generator  108  is furthermore configured to generate a first latent vector  120  from the trained generative model  118  based on the input image  10  of the selected retail item  12 , and to generate a second latent vector  122  from the trained generative model  118  based on the sample target image  20 . 
     The latent vector generator  108  is communicatively coupled to a latent vector modifier  110 . The latent vector modifier  110  is configured to modify the second latent vector  122  based on the first latent vector  120  to generate a modified latent vector  124 . The processor  104  further includes an image generator  112  configured to generate an output image  30  based on the modified latent vector  124 . 
     Referring again to  FIG. 1 , in one embodiment, a system  100  for transforming flat shot images of fashion retail items to catalogue images is presented. The system  100  includes an image acquisition unit  102  configured to receive a flat shot image  10  of a selected fashion retail item  12  and a sample catalogue image  20 . The system further includes a processor  104  operatively coupled to the image acquisition unit  102 . The processor  104  includes a training module  106 , a latent vector generator  108 , a latent vector modifier  110 , and an image generator  112 . The training module  106  is configured to train a generative adversarial network using a set of training flat shot images  114  and a set of training catalogue images  116 . The latent vector generator  108  is configured to generate a first latent vector  120  from the trained generative adversarial network  118  based on the flat shot image  10  of the selected retail item  12 , and to generate a second latent vector  122  from the trained generative adversarial network  118  based on the sample catalogue image  20 . The latent vector modifier  110  is configured to modify the second latent vector  122  based on the first latent vector  120  to generate a modified latent vector  124 ; and the image generator  112  is configured to generate an output catalogue image  30  based on the modified latent vector  124 . 
     The manner of implementation of the system  100  is described below in  FIGS. 2-10 .  FIG. 2  is a flowchart illustrating a method  200  for transforming images of retail items. The method  200  may be implemented using the system of  FIG. 1 , according to some aspects of the present description. Each step of the method  200  is described in detail below. 
     The method  200  includes, at step  202 , training a generative model using a set of training input images  114  and a set of training target images  116 . Non-limiting examples of suitable generative models include a Generative Adversarial Network, a cycle Generative Adversarial Network, or a bidirectional Generative Adversarial Network. In one embodiment, the generative model is a generative adversarial network (GAN). 
     A Generative Adversarial Network is neural network that includes a generative network and a discriminative network. A GAN may be used to generate images that look similar to the input data set by training the generator network and the discriminative network in competition. The generative network generates candidates (e.g., images) while the discriminative network evaluates them. Typically, the generative network learns to map from a latent space to a data distribution of interest, while the discriminative network distinguishes candidates (e.g., images) produced by the generator from the true data distribution. The generative network&#39;s training objective is to increase the error rate of the discriminative network, i.e., outwit the discriminator network by producing new images that the discriminator thinks are not synthesized (are part of the true data distribution). Backpropagation may be applied in both networks so that the generator produces better images, while the discriminator becomes more skilled at flagging synthetic images. The generator network and the discriminator network are trained until an equilibrium is reached. The trained network may be further used to generate a latent vector based on an image provided. The term “latent vector” as used herein refers to a dependent variable, whose value depends on a much smaller set of variables with a simpler probability distribution, like a vector of a dozen unit normal gaussians. This vector is typically denoted as “z”, the latent vector. Following the training of the GAN, the generator network can generate an image from a given latent vector. 
     In one embodiment, the method includes at step  202  initializing the GAN in the training module  106  and training the GAN using a set of training input images  114  and a set of training target images  116 . This ensures that the generator network is capable of generating both the input and target images. Since both these types of images are in the distribution learnt by the generator network, latent vectors corresponding to both the input and target images can be estimated using known methods. In one embodiment, the set of training input images  114  include standalone images of one or more retail items. As noted earlier, the term “standalone images” as used herein refers to the images of the one or more retail items by themselves. In embodiments related to fashion retail items, the “standalone images” do not include a model or a mannequin. In certain embodiments, set of training input images  114  may be flat shot images of the selected retail items. The flat shot images may be taken from any suitable angle and include top-views, side views, front-views, back-views, and the like. In another embodiment related to fashion retail items, the set of training input images  114  may be images of mannequins wearing the one or more retail items. 
     The set of training target images  116 , in such embodiments include corresponding catalogue images of the one or more retail items. The term “catalogue images” as used herein refers to images of the one or more retail items with the appropriate background etc for display in a product catalogue (either a printed catalogue or a digital catalogue). For example, for embodiments related to fashion retail items, the term “catalogue images” refers to images of the one or more retail items as worn by a model. The set of input training images  114  and the set of training target images  116  is presented to the generative model (e.g., GAN) in the training module  106 , at step  202 , and the model is trained to generate a trained generative model  118 . 
     The method  200  further includes, at step  204 , presenting an input image  10  of a selected retail item  12  to the trained generative model (e.g., a trained GAN) to generate a first latent vector  120 . The first latent vector may also be represented as “z_i.” The input image  10  may be accessed by the image acquisition unit  102  as discussed earlier and presented to the latent vector generator  108 . 
     For embodiments related to cataloguing of the selected retail items, the input image  10  may be selected by the user responsible for generating catalogue content. In such instances, the user may choose the input image  10  from an input image repository (not shown), or may capture the image  10  of the selected retail item  12  in real-time using a suitable imaging device. As mentioned earlier, the input image  10  may be a standalone image of the selected retail item  12  (e.g., a flat shot image) or may be an image of a mannequin wearing the selected retail item  12 . Further, the input images  10  may have been captured at various angles and the user may choose the appropriate input image based on the desired output catalogue image. The chosen image may be accessed by the image acquisition unit  102  as the input image  10  and presented to the trained generative model  118  in the latent vector generator  108 . For embodiments related to transformation of catalogue images to standalone images of the retail items, the input image  10  may be a catalogue image of the selected retail item  12  and the user may choose the input image from a repository of catalogue images. 
     Alternatively, for embodiments related to virtual try-on by the shopper, the input image  10  of the selected retail item may be chosen by the shopper, e.g., on an e-commerce platform (e.g., a web site, a mobile page, or an app). The shopper may search or browse the catalogue of retail items on the e-commerce platform and may select (e.g., by clicking on) an image of the selected retail item  12 . The selected image may be accessed by the image acquisition unit  102  as the input image  10  and presented to the trained generative model  118  in the latent vector generator  108 . 
       FIGS. 3-10  illustrate examples of different input images  10  according to embodiments of the present description.  FIGS. 3-7  show example embodiments where flat shot images of a selected retail item  12  are used as input images  10  to generate output catalogue images  30  of a model  22  wearing the selected retail item  12 .  FIG. 8  shows an example embodiment where an image of a mannequin  14  wearing the selected retail item  12  is used as the input image  10  to generate the output catalogue image  30  of a model  22  wearing the selected retail item  12 .  FIG. 9  shows an embodiment where a flat shot image of a selected retail item  12  is used as an input image  10  to generate an output image  30  of a shopper  26  wearing the selected retail item  12 .  FIG. 10  shows an embodiment where a catalogue image of a model  22  wearing the selected retail item  12  is used as an input image  10 . 
     The method  200  further includes, at step  206 , presenting a sample target image  20  to the trained generative model (e.g., a trained GAN)  118  to generate a second latent vector  122 . The second latent vector may also be represented as “z_t.” The sample target image  20  may be accessed by the image acquisition unit  102  as discussed earlier and presented to the latent vector generator  108 . 
     For embodiments related to cataloguing of the selected retail items, the sample target mage  20  is a sample catalogue image, and is selected based on one or more desired characteristics. In one embodiment, the sample target image  20  is an image of a model wearing another retail item. In such instances, the sample target image may be selected by the user responsible for generating catalogue content. The user may choose the sample target image  20  from a sample target image repository based on one or more desired characteristics of the output catalogue image. For example, for retail items such as furniture items the sample target image  20  may have the desired background required in the final output image. Similarly, for cataloguing of fashion retail items, the sample target image  20  may have the characteristics (e.g., model attributes, background etc) desired for the final catalogue image. In one example embodiment related to fashion retail items, the one or more desired characteristics include model pose, model skin tone, model body weight, model body shape, other retail items worn by the model, or background of the catalogue image. The selected image may be accessed by the image acquisition unit  102  as the sample target image  10  and presented to the trained generative model  118  in the latent vector generator  108 .  FIGS. 3-5 and 8  show example embodiments where images of a model  22  wearing another retail item  24  are used as sample target images  20 . 
     Alternatively, for embodiments related to virtual try-on by the shopper, the sample target image  20  is an image of the shopper wearing another retail item. In such instances, the sample target image  20  may be uploaded by the shopper, e.g., on the user interface of an e-commerce web platform (e.g., a web site, a mobile page, or an app). The uploaded image may be accessed by the image acquisition unit  102  as sample target image  20  and presented to the trained generative model  118  in the latent vector generator  108 .  FIG. 9  shows an embodiment where an image of a shopper  26  wearing another retail item  28  is used as the sample target image  20 . 
     Referring again to  FIG. 2 , the method  200  further includes, at step  208 , modifying the second latent vector  122  based on the first latent vector  120  to generate a modified latent vector  124 . As mentioned earlier, the latent vector generator generates a first latent vector z_i and a second latent vector z_t. The latent vector modifier modifies the second latent vector z_t by determining the part of z_t that corresponds to the other retail item  24 ,  28  worn by the model  22  or the shopper  26 . This part is replaced with z_i to generate the modified latent vector z_m. This can be achieved via several means. For every catalogue image for which the corresponding flat shot image is available (most e-commerce platforms have these images), the latent vector of the flat shot image can be subtracted from that of the catalogue image (z_t) to obtain the resultant latent vector. The latent vector of the retail image (z_i) to be transformed can be added to the resultant latent vector to give the modified latent vector (z_m). In cases where the flat shot image is not available, e.g., for a customer uploaded image, suitable methods may be used to modify the corresponding latent vector. 
     The method  200 , further includes at step  210 , generating an output image  30  based on the modified latent vector  124  (z_m). The method may further include displaying the output image  30  on a display unit to the user or the shopper.  FIGS. 3-8  show the output catalogue images  30  of a model  22  wearing the selected retail item  12 .  FIG. 9  shows the output image  30  as an image of the shopper  26  wearing the selected retail item  12 .  FIG. 10  shows the output image  30  as a standalone image of the selected retail item  12 . 
     For embodiments related to cataloguing of the selected retail items, the output image  30  may be further stored in a repository. In some embodiments, the steps  202  to  210  of the method  200  in such cases may be repeated for other input images  10  of the selected retail item  12  (e.g., with other angles) or for other selected target images  20  (e.g., with different model pose, accessories, background etc.) In some other embodiments, the user may select another retail item and steps  202  to  210  of the method  200  may be repeated for input images  10  of the other selected retail item resulting in a library of catalogue images of different retail items. The output images  30  may be incorporated into a catalogue layout and printed; or a plurality of static web pages including one or more output catalogue images may be generated, and those web pages may be served to visitors on an e-commerce platform (e.g., a web site, a mobile page, or an app). Thus, the systems and methods of the present description, may enable faster and cost-effective cataloguing of retail items, by digitally generating catalogue image data, and thus obviating the need for actual photo shoots. 
     For embodiments related to virtual try-on of the selected retail item  12 , the output image  30  may be displayed to the shopper on an e-commerce platform. If the shopper decides to purchase the selected retail item  12 , the information regarding the selected retail item  12  may be passed to an order-fulfillment process for subsequent activity. Alternately, the shopper may decide not to purchase the selected retail item and may choose another retail item for virtual try-on. In such instances, the steps  202 - 210  of the method  200  may be repeated for another retail item selected by the shopper. Thus, the systems and methods of the present description may enable the shopper to virtually try-on the selected retail items by generating images of the shopper wearing the selected retail items. 
     The different embodiments according to the present description are further illustrated in  FIGS. 3-10 . 
       FIG. 3  illustrates an example embodiment for generating a catalogue image of a dress  12  from a flat shot image  10  of the dress  12 . As mentioned earlier, although the image  10  shows a front view of the dress  12 , systems and methods of the present description are applicable for images taken from different angles (e.g., top view, side view, back view) as well. The flat shot image  10  of the dress  12  is presented to the latent vector generator  108  of  FIG. 1  to generate a first latent vector  120  (z_i). Further, the image  20  of a model  22  wearing another dress  24  of a different style is selected as the sample target image  20 . The sample target image  20  in this instance may be chosen, e.g., based on the desired pose of the model  22  in the output catalogue image  30 . This sample target image  20  is presented to the latent vector generator  108  of  FIG. 1  to generate a second latent vector  120  (z_t). The latent vector modifier  110  of  FIG. 1  modifies the z_t by replacing the part of z_t that corresponds to the dress  24  with the latent vector z_i, thereby generating a modified latent vector  124  (z_m). The modified latent vector z_m is used to generate the output catalogue image  30  that now shows the model  22  wearing the dress  12 . 
       FIG. 4-6  illustrate example embodiments where output catalogue images  30  with different model poses and/or accessories may be generated using a single input image.  FIG. 4  illustrates an embodiment for generation of a catalogue image of a dress  12  from a flat shot image  10  of the dress  12  except that the model pose in the output catalogue image  30  is changed, i.e., the back of the model is shown.  FIG. 5  shows an embodiment where different output catalogue images  30  with different model poses (including whether the model is facing the camera or turned to one side, or the position of the arms or legs) are generated.  FIG. 6  shows an embodiment where catalogue images  30  with different combinations of accessories  32  (e.g., shoes) and model poses are generated from the flat shot image  10  of the selected dress  12 , using the embodiments described herein. 
       FIG. 7  illustrates an example embodiment for generating a catalogue image of a hand bag  12  from a flat shot image  10  of the hand bag  12 . Similar to  FIG. 3 , a flat shot image  10  of the hand bag  12  is presented to the latent vector generator  108  of  FIG. 1  to generate a first latent vector  120  (z_i). Further, the image  20  of a model  22  holding another hand bag  24  of a different style is selected as the sample target image  20 . The sample target image  20  in this instance may be chosen, e.g., based on the desired pose of the model  22  in the output catalogue image  30 . This sample target image  20  is presented to the latent vector generator  108  of  FIG. 1  to generate a second latent vector  120  (z_t). The latent vector modifier  110  of  FIG. 1  modifies the z_t by replacing the part of z_t that corresponds to the hand bag  24  with the latent vector z_i, thereby generating a modified latent vector  124  (z_m). The modified latent vector z_m is used to generate the output catalogue image  30  that now shows the model  22  holding the hand bag  12 . 
       FIG. 8  shows an example embodiment where the input image  10  is an image of a mannequin  14  wearing a skirt  12 . The image  10  of the mannequin  14  is presented to the latent vector generator  108  of  FIG. 1  to generate a first latent vector  120  (z_i). Further, the image  20  of a model  22  wearing another skirt  24  of a different style is selected as the sample target image  20 . The sample target image  20  in this instance may be chosen, e.g., based on the desired pose of the model  22  in the output catalogue image  30 . This sample target image  20  is presented to the latent vector generator  108  of  FIG. 1  to generate a second latent vector  120  (z_t). The latent vector modifier  110  of  FIG. 1  modifies the z_t by replacing the part of z_t that corresponds to the skirt  24  with the latent vector corresponding to the skirt  12  in z_i, thereby generating a modified latent vector  124  (z_m). The modified latent vector z_m is used to generate the output catalogue image  30  that now shows the model  22  wearing the skirt  12 . 
       FIG. 9  illustrates an example embodiment that enables a shopper  26  to virtually try-on a dress  12 . The flat shot image  10  of the dress  12  is presented to the latent vector generator  108  of  FIG. 1  to generate a first latent vector  120  (z_i). Further, the image  20  of the shopper  26  wearing another dress  28  of a different style is presented to the latent vector generator  108  of  FIG. 1  to generate a second latent vector  120  (z_t). The sample target image  20  in this instance may be provided by the shopper  26 . The latent vector modifier  110  of  FIG. 1  modifies the z_t by replacing the part of z_t that corresponds to the dress  28  with the latent vector z_i, thereby generating a modified latent vector  124  (z_m). The modified latent vector z_m is used to generate the output image  30  that now shows the shopper  26  wearing the dress  12 . 
       FIG. 10  illustrates an embodiment for generating a standalone image  30  of a skirt  12  from a catalogue image  10  of the skirt  12 . 
     The system(s), described herein, may be realized by hardware elements, software elements and/or combinations thereof. For example, the modules and components illustrated in the example embodiments may be implemented in one or more general-use computers or special-purpose computers, such as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), a programmable logic unit (PLU), a microprocessor or any device which may execute instructions and respond. A central processing unit may implement an operating system (OS) or one or more software applications running on the OS. Further, the processing unit may access, store, manipulate, process and generate data in response to execution of software. It will be understood by those skilled in the art that although a single processing unit may be illustrated for convenience of understanding, the processing unit may include a plurality of processing elements and/or a plurality of types of processing elements. For example, the central processing unit may include a plurality of processors or one processor and one controller. Also, the processing unit may have a different processing configuration, such as a parallel processor. 
     Embodiments of the present description provide for improved systems and methods for generating image data for e-commerce platforms. More specifically, systems and methods of the present description, according to some embodiments, may enable faster and cost-effective cataloguing of retail items, by generating image data using generative models, and thus obviating the need for actual photo shoots. Further, in some embodiments, systems and methods of the present description may enable a shopper to virtually try-on fashion retail items by generating an image of the shopper wearing the selected retail item using generative models. 
     While only certain features of several embodiments have been illustrated, and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the scope of the invention and the appended claims.