Patent Publication Number: US-11030782-B2

Title: Accurately generating virtual try-on images utilizing a unified neural network framework

Description:
BACKGROUND 
     Providing interactive shopping experiences has become an important problem for developers in recent years. Consequently, several digital image systems have developed to deliver smart, intuitive online experiences including clothing retrieval, compatibility prediction, and virtual try-on to visualize products in a personalized setting. For example, some conventional digital image systems can enable a user to virtually try-on a specific garment. 
     Despite these advances however, conventional digital image systems continue to suffer from a number of disadvantages in accuracy, efficiency, and flexibility. For example, conventional digital image systems often inaccurately generate digital images that depict blurry or bleeding garment textures that result in unrealistic appearances of virtual clothing. Indeed, because of the complexity of synthesizing entire digital images to depict garment textures, these conventional systems often fail to properly account for bounds between different textures of a digital image, which often results in one texture blurring or bleeding into another. These conventional systems therefore generate images with deformed garment textures that exceed and/or do not adequately fill proper bounds of where the garment should fit. 
     In addition to their inaccuracy, many conventional digital image systems are also inefficient. To elaborate, many conventional systems rely on three-dimensional information pertaining to a model and/or a clothing item to then render a two-dimensional digital image representing the clothing item on the model. Conventional systems often require large amounts of computer resources to store and process such three-dimensional information. As the numbers of digital images (for models and/or products) increases (e.g., for online stores or catalogs), the expense of these conventional systems becomes more onerous. 
     Beyond their inaccuracy and inefficiency, many conventional digital image systems are also inflexible. For example, conventional systems often depend on three-dimensional information relating to either a model, a product, or both to utilize for rendering a virtual try-on image. However, three-dimensional information for products and models can be difficult to obtain and expensive to generate, which limits the range of application for these conventional systems. Indeed, because many of these conventional systems are tied to three-dimensional information, these systems cannot accurately generate virtual try-on images where such information is unavailable, which, due to the scarcity of three-dimensional information, significantly reduces their utility. 
     Thus, there are several disadvantages with regard to conventional digital image systems. 
     SUMMARY 
     One or more embodiments described herein provide benefits and solve one or more of the foregoing or other problems in the art with systems, methods, and non-transitory computer readable media that can accurately generate virtual try-on images utilizing a unified neural network framework. In particular, the disclosed systems can utilize a two-stage neural network framework that includes a warping stage and a texture transfer stage to generate a virtual try-on digital image that depicts a product digital image modified to fit a shape and pose of a model digital image. To this end, the disclosed systems can implement a coarse-to-fine warping process to warp a product digital image to align with a shape and a pose of a model digital image. In addition, the disclosed systems can implement a texture transfer process to transfer a warped product digital image to fit a model digital image. The disclosed systems can thus efficiently, flexibly, and accurately generate virtual try-on images. 
     Additional features and advantages of one or more embodiments of the present disclosure are outlined in the description which follows, and in part will be obvious from the description, or may be learned by the practice of such example embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       This disclosure will describe one or more embodiments of the invention with additional specificity and detail by referencing the accompanying figures. The following paragraphs briefly describe those figures, in which: 
         FIG. 1  illustrates an example system environment for implementing a virtual try-on digital image generation system in accordance with one or more embodiments; 
         FIG. 2A  illustrates a flawed virtual try-on digital image generated by a conventional digital image system in accordance with one or more embodiments; 
         FIG. 2B  illustrates an accurate virtual try-on digital image generated by the virtual try-on digital image generation system in accordance with one or more embodiments; 
         FIG. 3  illustrates a coarse-to-fine warping process in accordance with one or more embodiments; 
         FIG. 4  illustrates jointly training a coarse regression neural network and a fine regression neural network in accordance with one or more embodiments; 
         FIG. 5  illustrates a representation of feature space relationships for a perceptual geometric matching loss in accordance with one or more embodiments; 
         FIG. 6  illustrates generating a corrected segmentation mask in accordance with one or more embodiments; 
         FIG. 7  illustrates training a neural network to generate accurate corrected segmentation masks in accordance with one or more embodiments; 
         FIG. 8  illustrates generating a virtual try-on digital image in accordance with one or more embodiments; 
         FIG. 9  illustrates training a neural network to generate accurate virtual try-on digital images in accordance with one or more embodiments; 
         FIG. 10  illustrates a table of improvements associated with the virtual try-on digital image generation system in accordance with one or more embodiments; 
         FIG. 11  illustrates a schematic diagram of a virtual try-on digital image generation system in accordance with one or more embodiments; 
         FIGS. 12-13  illustrate flowcharts of series of acts for generating a virtual try-on digital image in accordance with one or more embodiments; and 
         FIG. 14  illustrates a block diagram of an example computing device in accordance with one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     One or more embodiments described herein provide benefits and solve one or more of the foregoing or other problems in the art with a virtual try-on digital image generation system that can generate virtual try-on digital images depicting products fit to model digital images utilizing a unified neural network framework. In particular, the virtual try-on digital image generation system can generate a virtual try-on digital image that depicts a model digital image with pixels replaced by a warped product digital image such that a model in the model digital image appears to be wearing a product from the product digital image. For example, the virtual try-on digital image generation system can utilize a coarse-to-fine warping process to warp a product digital image to fit a model digital image based on a pose and a shape of the model digital image. In addition, the virtual try-on digital image generation system can generate a virtual try-on digital image that depicts the warped product digital image on or combined with (e.g., replacing pixels of) the model digital image. For example, the virtual try-on digital image generation system can utilize a texture transfer process to fit the warped product digital image such that the warped product digital image aligns with, and replaces particular corresponding pixels of, the model digital image. 
     As just mentioned, the virtual try-on digital image generation system can generate a warped product digital image from an initial product digital image. More specifically, the virtual try-on digital image generation system can implement a coarse-to-fine warping process to warp the product digital image. To elaborate, the virtual try-on digital image generation system can utilize a coarse-to-fine warping process that consists of a two-stage warping procedure. First, the virtual try-on digital image generation system can determine coarse transformation parameters for coarse warping of the product digital image—i.e., to generate a coarse warped product digital image. Second, the virtual try-on digital image generation system can determine fine transformation parameters for fine warping of the product digital image to, combined with the coarse transformation parameters, generate a fine warped product digital image. 
     Regarding the coarse transformation parameters, the virtual try-on digital image generation system can utilize a coarse regression neural network to generate a coarse offset matrix that indicates coarse offsets or coarse transformation parameters. For example, the virtual try-on digital image generation system utilizes the coarse regression neural network to determine coarse modifications to make to portions of the product digital image to align the product digital image with a shape and a pose of the model digital image. Indeed, the virtual try-on digital image generation system can utilize a coarse regression neural network to analyze the product digital image along with digital image priors associated with the model digital image. For instance, the virtual try-on digital image generation system can generate digital image priors for the model digital image to determine a shape and a pose of the model digital image. Thus, the virtual try-on digital image generation system can utilize the digital image priors as guidance for aligning the product digital image with the model digital image. 
     In relation to the fine transformation parameters, the virtual try-on digital image generation system can utilize a fine regression neural network to generate a fine offset matrix that indicates fine offsets or fine transformation parameters. For example, the virtual try-on digital image generation system utilizes the fine regression neural network to determine fine modifications to make to portions of a product digital image to more closely align the product digital image with a shape and a pose of a model digital image. Indeed, the virtual try-on digital image generation system can determine additional fine modifications to make on top of the coarse modifications. Thus, within the coarse-to-fine warping process, the virtual try-on digital image generation system generates a coarse warped product digital image based on the coarse regression neural network and further generates a fine warped product digital image based on the fine regression neural network together with the coarse regression neural network. 
     Additionally, the virtual try-on digital image generation system can train the coarse regression neural network and the fine regression neural network. In particular, the virtual try-on digital image generation system can train both neural networks to accurately predict modifications to make to product digital images. For example, the virtual try-on digital image generation system can jointly train the coarse regression neural network and the fine regression neural network using one or more loss functions such as a perceptual geometric matching loss function. 
     As also mentioned above, the virtual try-on digital image generation system can generate a virtual try-on digital image that depicts a (fine) warped product digital image combined with a model digital image such that a model depicted in the model digital image appears to be wearing a product from the product digital image. In particular, the virtual try-on digital image generation system can utilize a texture transfer process that includes multiple constituent procedures to render a warped product digital image on top of (or in place of) particular pixels of a model digital image such that the product depicted within the product digital image appears realistically placed on a model of the model digital image. For example, the virtual try-on digital image generation system can generate a corrected (or conditional) segmentation mask based on a product digital image and digital image priors of a model digital image. In addition, the virtual try-on digital image generation system can utilize the corrected segmentation mask in addition to a product digital image and a fine warped version of the product digital image to generate an output of a virtual try-on digital image. 
     As mentioned, the virtual try-on digital image generation system can generate a corrected or conditional segmentation mask. In particular, the virtual try-on digital image generation system can generate an initial (non-corrected or non-conditional) segmentation mask based on digital image priors of a model digital image. The segmentation mask can indicate portions or pixels of the model digital image that are covered by a particular product. For example, the segmentation mask can delineate between which pixels are covered by a product within the model digital image and which pixels are covered by other textures. In some embodiments, the virtual try-on digital image generation system can represent pixels of the product (e.g., a shirt) with a first representation (e.g., a first color) and pixels corresponding to other textures (e.g., arms, pants, face) within the model digital image using different respective representations (e.g., different colors). 
     Based on the segmentation mask, the virtual try-on digital image generation system can utilize a neural network to generate a corrected segmentation mask. In particular, the virtual try-on digital image generation system can determine how to correct the initial segmentation mask based on a (fine) warped product digital image to indicate portions or pixels of the model digital image that are to be replaced by the warped product digital image. Indeed, the virtual try-on digital image generation system can modify the bounds of the initial segmentation mask to, for example, change the area of the model digital image that is to be replaced. For example, the virtual try-on digital image generation system can modify an initial segmentation mask depicting a short-sleeve-shirt-covered area to generate a corrected segmentation mask depicting a long-sleeve-shirt-covered area (for a product digital image of a long sleeve shirt) to be replaced. To this end, the virtual try-on digital image generation system can train the neural network to generate accurate corrected segmentation masks based on model digital image priors, product digital images, and ground truth segmentation masks. 
     Additionally, the virtual try-on digital image generation system can generate a virtual try-on digital image based on a corrected segmentation mask, a fine warped product digital image, and a model digital image. For example, the virtual try-on digital image generation system can translate a texture of the fine warped product digital image onto the model digital image in accordance with the pixels indicated by the corrected segmentation mask that are to be replaced. Indeed, the virtual try-on digital image generation system can train a neural network to generate accurate try-on digital images based on one or more loss functions such as a dueling triplet loss. 
     The virtual try-on digital image generation system can provide several advantages over conventional digital image systems. For example, the virtual try-on digital image generation system is more accurate than conventional systems. In particular, by utilizing a corrected segmentation mask that more clearly delineates bounds of pixels to be replaced by a product digital image, the virtual try-on digital image generation system is less prone to generating try-on digital images with bleeding or blurry pixels as compared to conventional systems. In addition, the virtual try-on digital image generation system can more accurately render a warped product digital image onto a model digital image by utilizing a coarse-to-fine warping process, whereby the virtual try-on digital image generation system identifies fine-grained shaped intricacies of a product digital image for modifying to fit a model digital image. 
     In addition, the virtual try-on digital image generation system is more efficient than conventional digital image systems. Whereas many conventional systems process resource-intensive three-dimensional information for a model and/or a product to generate try-on renders, the virtual try-on digital image generation system can utilize two-dimensional digital image information which is less resource-intensive. Indeed, because processing two-dimensional information requires fewer parameters than processing three-dimensional information, the virtual try-on digital image generation system requires fewer computer resources such as computing time, processing power, and memory. 
     On top of improved accuracy and efficiency, the virtual try-on digital image generation system is also more flexible than conventional digital image systems. In contrast with many conventional systems that are tied to three-dimensional information (which is very model-specific or product-specific and is sometimes difficult to obtain), the virtual try-on digital image generation system is capable of generating a more generalized framework for rendering try-on digital images. Indeed, by utilizing more widely available and more flexible two-dimensional information, the virtual try-on digital image generation system can determine generalized versions of texture transfers that can be adapted for different model digital images and/or different product digital images. 
     As illustrated by the foregoing discussion, the present disclosure utilizes a variety of terms to describe features and benefits of the virtual try-on digital image generation system. Additional detail is hereafter provided regarding the meaning of these terms as used in this disclosure. In particular, the term “digital image” refers to a digital visual representation (e.g., a visual portrayal of an object, a scene, or a person). A digital image can include a plurality of pixels that depict individual colors. A digital image can include a single still image or multiple digital images such as frames of a digital video. In some embodiments, a digital image can include a “model digital image” that depicts a person or a model. For example, a model digital image can include representation of a particular person such as an uploaded digital image of a user. In these or other embodiments, a digital image can include a “product digital image” depicting a particular product such as an item of clothing (e.g., a shirt, pants, or a hat) or some other product such as glasses, a purse, or jewelry. Additionally, a digital image can include a “virtual try-on digital image” (or “try-on digital image”) that refers to a digital image that depicts a model digital image with pixels replaced by a product of a product digital image. For example, a virtual try-on digital image can portray a model of a model digital image such that the model appears to be wearing a product of a (warped) product digital image. 
     As mentioned, the virtual try-on digital image generation system can determine transformation parameters associated with a product digital image. As used herein, the term “transformation parameter” refers to an operation, displacement, or transformation that the virtual try-on digital image generation system can apply to a portion of a digital image to change its appearance. Indeed, transformation parameters can include a transformation that describes a relationship between an initial appearance of a pixel (or group of pixels) and a resultant appearance after applying the transformation parameters. Transformation parameters can include coarse transformation parameters corresponding to coarse modifications to make to a product digital image and/or fine transformation parameters corresponding to fine modifications to make to a product digital image. 
     To determine transformation parameters, the virtual try-on digital image generation system can utilize a regression neural network to generate an offset matrix. For example, the virtual try-on digital image generation system can utilize a coarse regression neural network to generate a coarse offset matrix. Similarly, the virtual try-on digital image generation system can utilize a fine regression neural network to generate a fine offset matrix. As used herein, the term “offset matrix” (e.g., a “coarse offset matrix” or a “fine offset matrix”) refers to a matrix of transformation parameters. For example, an offset matrix can include a number of different offsets or other transformations to apply to a portion of a product digital image to modify its appearance. 
     Relatedly, the term “neural network” refers to a machine learning model that can be trained and/or tuned based on inputs to determine classifications or approximate unknown functions. In particular, the term neural network can include a model of interconnected artificial neurons (e.g., organized in layers) that communicate and learn to approximate complex functions and generate outputs (e.g., determinations of digital image classes) based on a plurality of inputs provided to the neural network. In addition, a neural network can refer to an algorithm (or set of algorithms) that implements deep learning techniques to model high-level abstractions in data. To illustrate, a neural network can include a deep convolutional neural network that includes constituent components (made up of one or more layers) such as an encoder, a decoder, a SoftMax layer, and an output layer. 
     As mentioned, the virtual try-on digital image generation system can train neural networks to generate warped product digital images, segmentation masks, and virtual try-on digital images. As used herein, the term “train” (or “trained” or “training”) refers to utilizing information to tune or teach a neural network by, for example, adjusting one or more weights or parameters of the neural network. 
     As further mentioned, the virtual try-on digital image generation system can generate digital image priors for use in various stages of generating a virtual try-on digital image such as determining a pose and a shape of a model digital image. As used herein, the term “digital image prior” (or simply “prior”) refers to particular information relating to a digital image such as an indication of a shape or a pose of a model within a model digital image. Indeed, the virtual try-on digital image generation system can determine digital image prior indicating shapes in the form of an outline of a model or figure within a model digital image. The virtual try-on digital image generation system can also determine pose priors indicating locations of particular anchor points or portions (e.g., shoulders, elbows, hips, neck, head, and hands) of a model within a model digital image. 
     Additionally, the virtual try-on digital image generation system can generate a segmentation mask based on a model digital image. As used herein, the term “segmentation mask” refers to a representation or indication of different segments or portions within a digital image. For example, a segmentation mask can indicate a difference between depicted textures such as a clothing texture, a skin texture, and a background texture shown in a model digital image. In some embodiments, a segmentation mask delineates bounds between a portion of a digital image to be replaced (e.g., pixels that depict a shirt to be replaced by a different shirt of a product digital image) and other portions not to be replaced (e.g., pixels that depict pants or arms or neck). A “corrected segmentation mask (or “conditional segmentation mask”) refers to a segmentation mask that has been corrected or conditioned based on a product digital image. For example, a corrected segmentation mask includes a segmentation mask where the initial portion to be replaced depicted a short-sleeve shirt and the corrected portion to be replaced depicts a long sleeve shirt. 
     Additional detail regarding the virtual try-on digital image generation system will now be provided with reference to the figures. For example,  FIG. 1  illustrates a schematic diagram of an example system environment or overall system for implementing a virtual try-on digital image generation system  102  in accordance with one or more embodiments. An overview of the virtual try-on digital image generation system  102  is described in relation to  FIG. 1 . Thereafter, a more detailed description of the components and processes of the virtual try-on digital image generation system  102  is provided in relation to the subsequent figures. 
     As shown, the system environment includes server(s)  104 , a client device  108  and a network  112 . Each of the components of the system environment can communicate via the network  112 , and the network  112  may be any suitable network over which computing devices can communicate. Example networks are discussed in more detail below in relation to  FIG. 14 . 
     As mentioned, the system environment includes a client device  108 . The client device  108  can be one of a variety of computing devices, including a smartphone, a tablet, a smart television, a desktop computer, a laptop computer, a virtual reality device, an augmented reality device, or another computing device as described in relation to  FIG. 14 . Although  FIG. 1  illustrates a single client device  108 , in some embodiments the environment can include multiple different client devices, each associated with a different user. The client device  108  can communicate with the server(s)  104  via the network  112 . For example, the client device  108  can receive user input from a user interacting with the client device  108  (e.g., via the client application  110 ) to request generation of a virtual try-on digital image. Thus, the virtual try-on digital image generation system  102  on the server(s)  104  can receive information or instructions to generate a virtual try-on digital image based on the input received by the client device  108 . 
     As shown, the client device  108  includes a client application  110 . In particular, the client application  110  may be a web application, a native application installed on the client device  108  (e.g., a mobile application, a desktop application, etc.), or a cloud-based application where all or part of the functionality is performed by the server(s)  104 . The client application  110  can present or display information to a user, including one or more digital images and/or user interface elements to edit or otherwise interact with a digital image(s). For example, the client application  110  can present an online catalog of model digital images and product digital images for browsing. A user can interact with the client application  110  to provide user input to, for example, change a product worn by a model in a model digital image (e.g., an uploaded digital image of the user). 
     As illustrated in  FIG. 1 , the system environment includes the server(s)  104 . The server(s)  104  may generate, track, store, process, receive, and transmit electronic data, such as model digital images, product digital images, and virtual try-on digital images. For example, the server(s)  104  may receive data from the client device  108  in the form of a request to generate a virtual try-on digital image. In addition, the server(s)  104  can transmit data to the client device  108  to provide a virtual try-on digital image. Indeed, the server(s)  104  can communicate with the client device  108  to transmit and/or receive data via the network  112 . In some embodiments, the server(s)  104  comprises a distributed server where the server(s)  104  includes a number of server devices distributed across the network  112  and located in different physical locations. The server(s)  104  can comprise a content server, an application server, a communication server, a web-hosting server, or a digital content management server. 
     As shown in  FIG. 1 , the server(s)  104  can also include the virtual try-on digital image generation system  102  as part of a digital content management system  106 . The digital content management system  106  can communicate with the client device  108  to generate, modify, and transmit digital content, such as model digital images, product digital images, and virtual try-on digital images. In addition, the digital content management system  106  and/or the virtual try-on digital image generation system  102  can train one or more neural networks such as regression neural networks and/or convolutional neural networks to perform coarse-to-fine warping and texture transfer. 
     Although  FIG. 1  depicts the virtual try-on digital image generation system  102  located on the server(s)  104 , in some embodiments, the virtual try-on digital image generation system  102  may be implemented by (e.g., located entirely or in part) on one or more other components of the system environment. For example, the virtual try-on digital image generation system  102  may be implemented by the client device  108  and/or a third-party device. 
     In some embodiments, though not illustrated in  FIG. 1 , the system environment may have a different arrangement of components and/or may have a different number or set of components altogether. For example, the client device  108  may communicate directly with the virtual try-on digital image generation system  102 , bypassing the network  112 . The system environment can also include a database or repository for storing digital images and other information. Additionally, the system environment can include one or more neural networks as part of the virtual try-on digital image generation system  102 , stored within a database, included as part of the client application  110 , or hosted on the server(s)  104 . 
     As mentioned, the virtual try-on digital image generation system  102  can generate a virtual try-on digital image as a combination of a model digital image and a product digital image such that a depicted model appears to be wearing the product of the product digital image. The virtual try-on digital image generation system  102  can generate a virtual try-on digital image that is more realistic and accurate than conventional baseline digital image systems. For example,  FIG. 2A  illustrates a representation of a flawed, conventional virtual try-on digital image generated by a conventional digital image system.  FIG. 2B  illustrates a more realistic virtual try-on digital image generated by the virtual try-on digital image generation system  102  in accordance with one or more embodiments. 
     As shown in  FIG. 2A , the conventional system generates a conventional virtual try-on digital image  206  where the shirt texture is blurry, bleeding below the pant line, and missing in portions of the model&#39;s hip, side, and shoulder. In addition, the conventional system fails to preserve the texture of the skirt in the model digital image  202 . As shown in the conventional virtual try-on digital image  206 , the skirt is generic and grey whereas the original skirt is patterned. Indeed, the conventional system attempts to combine the product digital image  204  of a shirt with the model digital image  202 , but due to the disadvantages of conventional systems described above, the resultant conventional virtual try-on digital image  206  is not accurate nor realistic. 
     As shown in  FIG. 2B , by contrast, the virtual try-on digital image generation system  102  generates a virtual try-on digital image  208  that is both accurate and realistic. Indeed, the virtual try-on digital image generation system  102  combines the model digital image  202  with the product digital image  204  to generate the virtual try-on digital image  208  that shows the model of the model digital image  202  wearing the shirt of the product digital image  204 . As shown, the virtual try-on digital image  208  accurately represents the shirt of the product digital image  204  warped to fit a shape and a pose of the model in the model digital image  202 , without blurry or bleeding portions. In addition, the virtual try-on digital image  208  preserves the texture of the skirt from the model digital image  202  to create a more accurate representation of a complete outfit. Indeed, users often want to virtually try-on shirts or other products to see how they complement an outfit as a whole, which is rendered possible by preserving the textures of the other components of the model digital image  202  by the virtual try-on digital image generation system  102 . 
     To generate the virtual try-on digital image  208 , the virtual try-on digital image generation system  102  can utilize multiple processes or methods together. For example, the virtual try-on digital image generation system  102  can utilize a coarse-to-fine warping process and a texture transfer process. Indeed, the virtual try-on digital image generation system  102  can utilize a coarse-to-fine warping process to generate a warped product digital image to fit a model digital image.  FIG. 3  illustrates a coarse-to-fine warping process that the virtual try-on digital image generation system  102  utilizes to generate a fine warped product digital image  322  in accordance with one or more embodiments. 
     As illustrated in  FIG. 3 , the virtual try-on digital image generation system  102  utilizes a coarse regression neural network  308  and fine regression neural network  320  as a multi-stage coarse-to-fine process for generating the fine warped product digital image  322 . Indeed, in one or more embodiments, the description of  FIG. 3 , including the disclosed algorithms, provide the corresponding structure for performing a step for coarse-to-fine warping of the product digital image  304  to align with the model digital image  302 . 
     As shown, the virtual try-on digital image generation system  102  identifies or receives a model digital image  302  (I m ) and a product digital image  304  (I p ). In particular, the virtual try-on digital image generation system  102  can access the model digital image  302  and the product digital image  304  from a repository of digital images. In some embodiments, the virtual try-on digital image generation system  102  receives the model digital image  302  as an upload from the client device  108  or captures the model digital image  302  via the client device  108 . In one or more embodiments, the virtual try-on digital image generation system  102  identifies the product digital image  304  from a website or an online catalog of product digital images. The virtual try-on digital image generation system  102  can further receive user input selecting the product digital image  304  and the model digital image  302  and requesting a virtual try-on digital image depicting the model of the model digital image  302  wearing the shirt of the product digital image  304 . 
     In addition, the virtual try-on digital image generation system  102  accesses or determines digital image priors  306  (I priors ) for the model digital image  302 . For example, the virtual try-on digital image generation system  102  determines shape priors as an outline of a shape of a model in the model digital image  302  and pose priors as locations of anchor points for joints or other portions of the model in the model digital image  302 . As shown in  FIG. 3 , the virtual try-on digital image generation system  102  determines shape priors in the form of a white silhouette outlining the shape of the model in the model digital image  302 . The virtual try-on digital image generation system  102  also determines pose priors in the form of points indicating particular portions of the model in the model digital image  302  such as a chin, a head, shoulders, hands, and hips to give an indication of the pose of the model. The digital image priors  306  can leave out effects of clothes (like color, texture, and shape), while preserving the person&#39;s face, hair, body shape, and pose. 
     In some embodiments, the digital image priors  306  are a 19-channel map of pose and body-shape map that the virtual try-on digital image generation system  102  generates using the model digital image  302  to overcome the unavailability of training triplets. For example, the digital image priors  306  can comprise a pose heatmap and a body representation. 
     The pose heatmap can comprise an 18-channel feature map with each channel corresponding to a human pose keypoint. To leverage the spatial layout, the virtual try-on digital image generation system  102  can transform each keypoint into a heatmap, with and 11×11 neighborhood around the keypoint filled with ones and zeroes everywhere else. In one or more embodiments, the virtual try-on digital image generation system generates the pose heatmap using a pose estimator, such as that described by Z. Cao, T. Simon, S.-E. Wei, and Y. Sheik in  Realtime Multi - person  2 D Pose Estimation Using Part Affinity Fields  in CVPR, 2017, the entire contents of which are hereby incorporated by reference in their entirety. 
     The body representation can comprise a one channel feature map of a blurred binary mask that roughly covers the shape of the person&#39;s body. The virtual try-on digital image generation system generates the body representation using a human parser to compute a human segmentation map, where different regions represent different parts of the human body (e.g., arms, legs). The virtual try-on digital image generation system can convert the human segmentation map to a 1-channel binary mask where ones indicate the human body. In one or more embodiments, the virtual try-on digital image generation system uses a human parser to generate the body representation, such as that described by K. Gong, X. Liang, X. Shen, and L. Lin in  Look into person: Self - supervised structure - sensitive learning and a new benchmark for human parsing  in CVPR, 2017, the entire contents of which are hereby incorporated by reference in their entirety. 
     As further illustrated in  FIG. 3 , the virtual try-on digital image generation system  102  inputs the digital image priors  306  and the product digital image  304  into a coarse regression neural network  308 . Based on analyzing the digital image priors  306  and the product digital image  304  using its various constituent components/layers, the coarse regression neural network  308  outputs a coarse warped product digital image  318 . In particular, the virtual try-on digital image generation system  102  generates the coarse warped product digital image  318  by (coarsely) modifying one or more portions of the product digital image  304  in accordance with coarse transformation parameters learned by the coarse regression neural network  308 . For example, the virtual try-on digital image generation system  102  modifies the product digital image  304  by moving portions to align with a shape and a pose of the model digital image  302  (as indicated by the digital image priors  306 ). 
     To elaborate, the virtual try-on digital image generation system  102  inputs the digital image priors  306  into a convolutional encoder  310   a  of the coarse regression neural network  308 , whereupon the convolutional encoder  310  encodes, extracts, or generates feature representations (e.g., including observable and/or hidden latent features) for the digital image priors  306 . Additionally, the virtual try-on digital image generation system  102  inputs the product digital image  304  into a convolutional encoder  310   b , whereupon the convolutional encoder  310   b  encodes, extracts, or generates feature representations for the product digital image  304 . 
     As shown, the virtual try-on digital image generation system  102  further utilizes a feature correlation  312  to correlate and/or combine features of the digital image priors  306  and the product digital image  304 . Thus, the virtual try-on digital image generation system  102  generates a combined feature representation for the product digital image  304  and the digital image priors  306  (or, by association, the model digital image  302 ). The virtual try-on digital image generation system  102  further utilizes a regressor  314  to determine coarse transformation parameters B that indicate coarse modifications to make to the product digital image  304  to align portions of the depicted shirt with the pose and shape of the digital image priors  306 . 
     In some embodiments, the virtual try-on digital image generation system  102  utilizes the coarse regression neural network  308  to generate the coarse transformation parameters θ in the form of a coarse offset matrix. In particular, the virtual try-on digital image generation system  102  generates a coarse offset matrix that includes coarse modifications for modifying portions of the product digital image to align with a pose and a shape of the model digital image. Indeed, different fields of the coarse offset matrix can include different offsets or other transformation parameters that indicate how to modify respective portions of the product digital image  304 . 
     Based on the coarse transformation parameters θ, the virtual try-on digital image generation system  102  further utilizes a thin-plate spline spatial transformer  316  to generate the coarse warped product digital image  318 . In particular, the virtual try-on digital image generation system  102  utilizes the thin-plate spline spatial transformer  316  to (coarsely) warp, transform, or modify portions of the product digital image  304  to align with the digital image priors  306  in accordance with the coarse transformation parameters B. In some embodiments, the virtual try-on digital image generation system  102  utilizes a thin-plate spline spatial transformer  316  as described by M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu in  Spatial Transformer Networks , Advances in Neural Information Processing Systems, 2017-25 (2015), which is incorporated by reference in its entirety. Two main differences, however, are that the virtual try-on digital image generation system  102  learns the transformation parameters θ and θ+Δθ in a two-stage cascaded structure and utilizes a novel perceptual geometric matching loss for training. 
     Based on generating the coarse warped product digital image  318  (I stn   0 ), the virtual try-on digital image generation system  102  can further generate a fine warped product digital image  322 . In particular, the virtual try-on digital image generation system  102  can determine fine modifications or fine transformations to make to the coarse warped product digital image  318  to more closely align the depicted product with the shape and the pose of the digital image priors  306  (or, by association, the model digital image  302 ). As shown in  FIG. 3 , the virtual try-on digital image generation system  102  inputs the coarse warped product digital image  318  into a fine regression neural network  320  to generate the fine warped product digital image  322  (I stn   1 ). 
     In particular, the virtual try-on digital image generation system  102  inputs the coarse warped product digital image  318  into a convolutional encoder  323  of the fine regression neural network  320 . The convolutional encoder  323 , in turn, encodes or generates a feature representations of the coarse warped product digital image  318  including observable features and/or hidden latent features. The virtual try-on digital image generation system  102  passes the features through a feature correlator  324  along with the digital image priors  306  to determine relationships or correlations between the features of the coarse warped product digital image  318  and the digital image priors  306 . 
     Based on these relationships, the virtual try-on digital image generation system  102  can determine how much warping or transformation is still required to align with the digital image priors  306 . Indeed, the virtual try-on digital image generation system  102  passes the correlated features/relationships to a regressor  326  to determine a difference or a change of the coarse warped product digital image  318  still required to align with the digital image priors  306 . Thus, the virtual try-on digital image generation system  102  generates the coarse transformation parameters Δθ that indicate additional fine-level warping, modifying, or transforming of the coarse warped product digital image  318 . In some embodiments, the virtual try-on digital image generation system  102  generates the fine transformation parameters Δθ in the form of a fine offset matrix that includes offsets or other transformations indicating what fine-level modifications to make to which respective portions of the coarse warped product digital image  318  (or the product digital image  304 ) to align with a pose and a shape of the digital image priors  306 . 
     As shown in  FIG. 3 , the virtual try-on digital image generation system  102  further combines the coarse transformation parameters θ with the fine transformation parameters Δθ (e.g., by adding them together). By combining the transformation parameters, the virtual try-on digital image generation system  102  determines how to modify the product digital image  304  to generate the fine warped product digital image  322 . To facilitate the expected hierarchical behavior, the virtual try-on digital image generation system  102  utilizes residual connections to offset the fine transformation parameters Δθ with the coarse transformation parameters θ. Additionally, as shown, the virtual try-on digital image generation system  102  inputs the product digital image  304  into the thin-plate spline spatial transformer  328  of the fine regression neural network  320  along with the combined transformation parameters θ+Δθ. 
     The thin-plate spline spatial transformer  328  thereby implements the modifications of the combined transformation parameters θ+Δθ to transform the product digital image  304  and generate the fine warped product digital image  322  that aligns with the shape and the pose of the model digital image  302 . By transforming the product digital image  304  instead of the coarse warped product digital image  318 , the virtual try-on digital image generation system  102  avoids artifacts that could otherwise result from applying the interpolation in the spatial transformer twice. In some embodiments, however the virtual try-on digital image generation system  102  can modify the coarse warped product digital image  318  to generate the fine warped product digital image  322 . 
     By utilizing the coarse-to-fine warping process of  FIG. 3 , the virtual try-on digital image generation system  102  generates an accurate fine warped product digital image  322  that accounts for occlusion and pose variation. Indeed, in some cases, there may be large variations in shape or pose between a product digital image (e.g., the product digital image  304 ) and corresponding portions of a model digital image (e.g., the model digital image  302 ). Additionally, a model digital image may include occlusions where long hair or different poses cause portions of a garment (or other product/portion of the image to be replaced) is occluded or blocked from view. Unlike conventional systems, the virtual try-on digital image generation system  102  utilizes the above-described coarse-to-fine warping process to accommodate these occlusion and variation problems to nevertheless generate accurate warped product digital images. 
     As mentioned, the virtual try-on digital image generation system  102  can utilize coarse transformation parameters θ and fine transformation parameters Δθ to modify a product digital image to align with a model digital image (i.e., to generate a fine warped product digital image). To generate accurate, realistic warped product digital images, the virtual try-on digital image generation system  102  trains the coarse regression neural network  308  and the fine regression neural network  320 . More specifically, because generating the fine warped product digital image  322  is based on the coarse transformation parameters Band the fine transformation parameters Δθ, the virtual try-on digital image generation system  102  trains the neural networks to accurately learn these parameters. Indeed, the virtual try-on digital image generation system  102  utilizes a novel loss function as part of the training process called a perceptual geometric matching loss function. 
       FIG. 4  illustrates a training process for jointly training the coarse regression neural network  308  and the fine regression neural network  320  learn coarse transformation parameters θ and fine transformation parameters Δθ in accordance with one or more embodiments. In particular, the virtual try-on digital image generation system  102  accesses a model digital image  402  (I m ) and a product digital image  404  (I P ) from a database  406 . The virtual try-on digital image generation system  102  inputs the model digital image  402  (or corresponding digital image priors) and the product digital image  404  into the coarse regression neural network  308 . The coarse regression neural network  308  generates a prediction of how to (coarsely) warp the product digital image  404  to fit the model digital image  402 —i.e., the coarse regression neural network  308  generates a predicted coarse warped product digital image  408 . 
     In addition, the virtual try-on digital image generation system  102  inputs the product digital image  404  and the predicted coarse warped product digital image  408  into the fine regression neural network  320 . The fine regression neural network  320  thereby generates a prediction of how to modify the product digital image  404  (based on coarse transformation parameters and fine transformation parameters) to align with the model digital image  402 . The fine regression neural network  320  thus generates a predicted fine warped product digital image  410 . 
     As shown, the virtual try-on digital image generation system  102  further performs a comparison  412  to compare the predicted coarse warped product digital image  408 , the predicted fine warped product digital image  410 , and a ground truth warped product digital image  414 . Indeed, the virtual try-on digital image generation system  102  generates the ground truth warped product digital image  414  by segmenting out a portion of the model digital image  402  that depicts a particular product (e.g., a shirt) to be replaced by the product digital image. The virtual try-on digital image generation system  102  thus utilizes the segmented-out portion of the model digital image  402  as the ground truth warped product digital image  414 . During training, the model digital image  402  and a product digital image  404  include the same product (e.g., shirt) so that the model digital image  402  can be used as a ground truth of how to warp the product in the product digital image  404 . 
     The virtual try-on digital image generation system  102  performs the comparison  412  to determine an error or measure of loss associated with the coarse regression neural network  308  and the fine regression neural network  320 . In particular, the virtual try-on digital image generation system  102  utilizes a particular loss function such as a warp loss (which includes perceptual geometric matching loss component) to determine the measure of loss associated with the predictions of the neural networks. Additional detail regarding the warp loss and the perceptual geometric matching loss is provided below with reference to  FIG. 5 . 
     Additionally, to improve the accuracy of the predictions, the virtual try-on digital image generation system  102  reduces or minimizes the measure(s) of loss associated with the coarse regression neural network  308  and the fine regression neural network  320 . More specifically, the virtual try-on digital image generation system  102  utilizes back propagation  416  to reduce the measure of loss. For example, the virtual try-on digital image generation system  102  modifies or adjusts one or more weights or parameters associated with particular components or layers of the coarse regression neural network  308  and the fine regression neural network  320 . By modifying the weights/parameters in this way, the virtual try-on digital image generation system  102  adjusts how the coarse regression neural network  308  and the fine regression neural network  320  learn transformation parameters via their respective components. Thus, upon subsequent training iterations, the coarse regression neural network  308  and the fine regression neural network  320  generate more accurate coarse warped predicted coarse warped product digital images and predicted fine warped product digital images. 
     Indeed, the virtual try-on digital image generation system  102  repeats the training process of  FIG. 4  for multiple iterations or epochs. To elaborate, the virtual try-on digital image generation system  102  identifies additional model digital images and product digital images for subsequent training iterations. The virtual try-on digital image generation system  102  repeats the process of utilizing the coarse regression neural network  308  to generate predicted coarse warped product digital images, utilizing the fine regression neural network  320  to generate predicted fine warped product digital images, performing the comparison  412 , and the back propagation  416 . Through this joint training process, the virtual try-on digital image generation system  102  continuously modifies weights/parameters of the neural networks to learn accurate coarse transformation parameters θ and fine transformation parameters Δθ until the measure of loss associated with the neural networks satisfies a loss threshold (or until the predicted coarse and fine product digital images satisfy a an accuracy threshold). 
     As mentioned above, the virtual try-on digital image generation system  102  can utilize a perceptual geometric matching loss as part of the comparison  412  to train the coarse regression neural network  308  and the fine regression neural network  320 .  FIG. 5  illustrates an example representation of implementing the perceptual geometric matching loss in accordance with one or more embodiments. As described above, the virtual try-on digital image generation system  102  generates feature representations for digital images utilizing the coarse regression neural network  308  and the fine regression neural network  320 . In particular, the virtual try-on digital image generation system  102  generates feature representations of the predicted coarse warped product digital image  408 , the predicted fine warped product digital image  410 , and the ground truth warped product digital image  414 . 
     As illustrated in  FIG. 5 , the virtual try-on digital image generation system  102  further compares the feature representations of these three digital images utilizing a perceptual geometric matching loss. More particularly, the virtual try-on digital image generation system  102  subjects the interim I stn   0  (the predicted coarse warped product digital image  408 ) and final I stn   1  (the predicted fine warped product digital image  410 ) output to a warp loss L warp  against I gt-warp  (the ground truth warped product digital image  414 ). The warp loss L warp  includes a perceptual geometric matching loss component L pgm . By utilizing the warp loss, the virtual try-on digital image generation system  102  causes the fine regression neural network  320  to incrementally improve upon the warping modifications (e.g., the coarse transformation parameters θ) of the coarse regression neural network  308 . 
     Indeed,  FIG. 5  illustrates the respective feature representations of I stn   0 , I stn   1 , and I gt-warp  in a VGG-19 feature space, where d 0 , d 1 , and d 01  represent distances or difference vectors between the feature representations in the feature space as shown. To elaborate, the virtual try-on digital image generation system  102  can utilize a warp loss function that includes a perceptual geometric matching loss, as represented by:
 
 L   warp =λ 1   L   s   0 +λ 2   L   s   1 +λ 3   L   pgm  
 
 L   s   0   =|I   gt-warp   −I   stn   0 |
 
 L   s   1   =|I   gt-warp   −I   stn   1 |
 
where λ n  represents a respective weight, I gt-warp =I m *M gt   product  and L pgm  is the perceptual geometric matching loss. In addition, I gt-warp  is the product depicted (worn) in the model digital image  402  (I m ) and M gt   product  is the binary mask representing the product worn by the model in the model digital image  402 .
 
     The virtual try-on digital image generation system  102  can further determine the perceptual geometric matching loss L pgm  in accordance with:
 
 L   pgm =λ 4   L   push +λ 5   L   align  
 
where L push  represents a push loss that moves the second stage output IL relative to the ground-truth I gt-warp  and L align  represents an alignment loss associated with how aligned the second stage output I stn   1  is relative to the ground-truth I gt-warp .
 
     In some embodiments, the virtual try-on digital image generation system  102  minimizes L push  to push the second stage output IL closer to the ground-truth I gt-warp  compared to the first stage output I stn   0 . The virtual try-on digital image generation system  102  can determine L push , as given by:
 
 L   push   =k*L   s   1   −|I   stn   1   −I   stn   0 |
 
where k is a scalar multiplicative margin that the virtual try-on digital image generation system  102  uses to ensure stricter bounds for the difference (e.g., k=3 is used for testing to obtain the results described below in relation to  FIG. 10 ).
 
     To determine L push , the virtual try-on digital image generation system  102  maps L push , I stn   0 , I stn   1  and I gt-warp  to the VGG-19 feature space. In addition, the virtual try-on digital image generation system  102  utilizes the loss to align the difference vectors between I stn   0  and I gt-warp  (the difference vector d 0 ) and between I stn   1  and I gt-warp  (the difference vector d 1 ). For example, in some embodiments the virtual try-on digital image generation system  102  aligns the difference vectors d 0  and d 1  by reducing or minimizing d 01 , the difference vector between I stn   0  and I stn   1 . 
     To minimize L push , the virtual try-on digital image generation system  102  can minimize L align  to help achieve this purpose. For example, the virtual try-on digital image generation system  102  can determine L align  in accordance with:
 
 d   0   =VGG ( I   stn   0 )− VGG ( I   gt-warp )
 
 d   1   =VGG ( I   stn   1 )− VGG ( I   gt-warp )
 
 L   align =(CosineSimilarity( d   0   ,d   1 )−1) 2  
 
where d 0  and d 1  are the difference vectors shown in  FIG. 5 .
 
     As mentioned above, the virtual try-on digital image generation system  102  can utilize a texture transfer process to generate a virtual try-on digital image by combining a fine warped product digital image with a model digital image such that the model in the model digital image appears to be wearing the product of the product digital image. The texture transfer process can include multiple stages, such as generation of a corrected segmentation mask (as described in relation to  FIG. 6 ) and segmentation-assisted texture translation (as described in relation to  FIG. 8 ). In one or more embodiments, the description of  FIG. 6  (in conjunction with additional description of  FIG. 8 ) provides the supporting structure (e.g., acts and algorithms) for performing a step for applying a texture transfer process to the warped product digital image and the model digital image to generate a virtual try-on digital image. 
     A key problem with many conventional systems is their inability to accurately honor the bounds of products and human skin, which causes product pixels to blur or bleed into the skin pixels (or vice-versa), and in the case of self-occlusion (such as with folded arms), skin pixels may get replaced entirely. This problem is exacerbated for cases where the product of a product digital image has a significantly different shape that a corresponding product to be replaced in a model digital image. Another scenario that aggravates this problem is when a model of a model digital image is in a complex pose. To help mitigate these problems of bleeding and self-occlusion, as well as accommodation of complex poses, the virtual try-on digital image generation system  102  utilizes a corrected (or conditional) segmentation mask prediction network. 
     Indeed,  FIG. 6  illustrates utilizing a neural network  602  to generate a corrected segmentation mask  608  (e.g., an expected segmentation map M exp ) in accordance with one or more embodiments. For instance, to transfer the texture of the fine warped product digital image  322  onto the model digital image  302 , the virtual try-on digital image generation system  102  can generate a segmentation mask for the model digital image  302  that indicates a portion (e.g., a number of pixels) of the model digital image  302  that are to be replaced with the texture of the fine warped product digital image  322 . As shown in  FIG. 6 , the virtual try-on digital image generation system  102  thus generates a corrected segmentation mask  608  for the model digital image  302 . 
     As illustrated in  FIG. 6 , the virtual try-on digital image generation system  102  utilizes the digital image priors  306  of the model digital image  302  along with the product digital image  304  to generate a corrected segmentation mask  608 . The corrected segmentation mask  608  indicates those pixels of the model digital image  302  that are to be replaced by the fine warped product digital image  322 . For instance, the light grey portion of the corrected segmentation mask  608  depicts a tank-top-shaped area that indicates a corresponding area of the model digital image  302  that is to be replaced by the warped product digital image  322 . In addition, the corrected segmentation mask  608  clearly delineates bounds between various portions of the model digital image  302 , as indicated by the various colors of the corrected segmentation mask  608  that each correspond to a different texture of the image. 
     To generate the corrected segmentation mask  608 , the virtual try-on digital image generation system  102  determines how to modify a segmentation mask  610  associated with the digital image priors  306  (or the model digital image  302 ). Indeed, the virtual try-on digital image generation system  102  generates, identifies, or accesses the segmentation mask  610  that, based on the digital image priors  306 , indicates portions of the model digital image  302  that are covered by (or depict) a particular product. For example, the segmentation mask  610  depicts a light grey portion (covering a torso area) that corresponds to the portion of the model digital image  302  that is covered by the long sleeve shirt. In one or more embodiments, the virtual try-on digital image generation system generates the segmentation mask  610  using a human parser to compute a segmentation map, where different regions represent different parts of the human body (e.g., arms, shirt, pants, legs). In one or more embodiments, the virtual try-on digital image generation system uses a human parser to generate the segmentation mask  610 , such as that described by K. Gong, X. Liang, X. Shen, and L. Lin in  Look into person: Self - supervised structure - sensitive learning and a new benchmark for human parsing  in CVPR, 2017, the entire contents of which are hereby incorporated by reference in their entirety. 
     To modify, condition, or correct the segmentation mask  610  and generate the corrected segmentation mask  608 , the virtual try-on digital image generation system  102  can utilize a neural network such as a convolutional neural network. Indeed, as shown in  FIG. 6 , the virtual try-on digital image generation system  102  inputs the digital image priors  306  and the product digital image  304  into the neural network  602 , whereupon the neural network  602  outputs the corrected segmentation mask  608 . In particular, the virtual try-on digital image generation system  102  utilizes the neural network  602  to determine relationships between the digital image priors  306  and the product digital image  304  to determine a portion of the model digital image  302  that is expected to be covered by the product digital image  304 . For example, the neural network  602  determines classes for various portions of the model digital image  302  based on the digital image priors  306  and the product digital image  304 —e.g., by classifying skin textures distinctly from hair textures, pants textures, shirt textures, and background textures. 
     For example, the virtual try-on digital image generation system  102  inputs the digital image priors  306  and the product digital image  304  into a convolutional encoder  604  that encodes or extracts features of the digital image priors  306  and the product digital image  304 . In addition, the virtual try-on digital image generation system  102  passes the extracted features to an upsampling convolutional decoder  606  that generates (or causes to be generated) the corrected segmentation mask  608  based on the relationships between the features of the digital image priors  306  and the features of the product digital image  304 . Although  FIG. 6  illustrates just two components of the neural network  602  (the convolutional encoder  604  and the upsampling convolutional decoder  606 ), in some embodiments, the neural network  602  includes additional or alternative components or layers. For example, the neural network  602  can include a 12-layer U-Net-like architecture as described by O. Ronneberger, P. Fischer, and T. Brox in  U - Net: Convolutional Neural Networks for Biomedical Image Segmentation , Int&#39;l Conf. on Medical Image Computing and Computer-Assisted Intervention, 234-41 (2015), which is incorporated by reference herein in its entirety. 
     To ensure that the neural network  602  accurately generates the corrected segmentation mask  608 , the virtual try-on digital image generation system  102  can train the neural network  602 .  FIG. 7  illustrates training the neural network  602  in accordance with one or more embodiments. As shown, the virtual try-on digital image generation system  102  inputs, from the database  406 , model digital image priors and a product digital image  704  into the neural network  602 . The neural network  602  analyzes the model digital image priors  702  and the product digital image  704  to generate a predicted segmentation mask  706 . The predicted segmentation mask  706  represents a prediction of what the neural network  602  expects for a segmentation mask according to its various weights and parameters. 
     Additionally, the virtual try-on digital image generation system  102  performs a comparison  708  to compare the predicted segmentation mask  706  with a ground truth segmentation mask  710 . Indeed, the virtual try-on digital image generation system  102  accesses a ground truth segmentation mask  710  that corresponds to the model digital image priors  702  and/or the product digital image  704  from the database  406 . Thus, the virtual try-on digital image generation system  102  can utilize a cross entropy loss function to compare the predicted segmentation mask  706  with the ground truth segmentation mask  710  to thereby determine a measure of loss associated with the neural network  602 . For instance, the virtual try-on digital image generation system  102  can utilize a cross entropy loss function for semantic segmentation with increased weights for skin classes (to better handle occlusion cases) and background classes (to stem bleeding of skin pixels into other pixels). 
     Further, the virtual try-on digital image generation system  102  performs back propagation  712  to reduce or minimize the measure of loss determined via the comparison  708 . In particular, the virtual try-on digital image generation system  102  modifies weights or parameters of various layers/components of the neural network  602  to adjust how the neural network  602  analyzes digital image priors and product digital images. As a result, neural network  602  utilizes the modified weights/parameters on subsequent training iterations to determine classes for various textures. Thus, after multiple training iterations or epochs, the virtual try-on digital image generation system  102  modifies the weights/parameters of the neural network  602  to the point where the neural network  602  generates accurate predicted segmentation masks (e.g., where the measure of loss satisfies a threshold). 
     Because the neural network  602  learns from sparse product-agnostic input (e.g., I priors ), that does not include effects of a model digital image (to avoid learning identity), the virtual try-on digital image generation system  102  can generalize to unseen models (e.g., model digital images other than those on which the neural network  602  is specifically trained). Indeed, the virtual try-on digital image generation system  102  is flexible enough to generate virtual try-on digital images for model digital images and product digital images that are not necessarily part of a set of training data. 
     As mentioned, the virtual try-on digital image generation system  102  can utilize the corrected segmentation mask  608  as part of generating a virtual try-on digital image where a model appears to be wearing a product of a product digital image.  FIG. 8  illustrates generating a virtual try-on digital image  814  in accordance with one or more embodiments. As mentioned above, the description of  FIG. 8 , along with that of  FIG. 6  (and other portions of this disclosure) can provide algorithms and structure for performing a step for applying a texture transfer process to the warped product digital image and the model digital image to generate a virtual try-on digital image. 
     As shown in  FIG. 8 , the virtual try-on digital image generation system  102  can utilize a neural network  804  to generate the virtual try-on digital image  814 . More specifically, like the neural network  602  described above, the neural network  804  can include a 12-layer U-Net as described by O. Ronneberger et al. in  U - Net: Convolutional Neural Networks for Biomedical Image Segmentation . For example, the virtual try-on digital image generation system  102  inputs the fine warped product digital image  322 , the corrected segmentation mask  608 , and texture translation priors  802  of the model digital image  302  into the neural network  804 . The virtual try-on digital image generation system  102  generates or identifies the texture translation priors  802  which can include pixels of the model digital image  302  that are unaffected such as face pixels and pixels of a product not being replaced in the model digital image  302  (e.g., pants in the illustrated case). In one or more embodiments, the virtual try-on digital image generation system  102  generates the texture translation priors  802  using a human parser, such as that described by K. Gong, X. Liang, X. Shen, and L. Lin in  Look into person: Self - supervised structure - sensitive learning and a new benchmark for human parsing  in CVPR, 2017, the entire contents of which are hereby incorporated by reference in their entirety 
     As shown, the virtual try-on digital image generation system  102  utilizes a convolutional encoder  806  to extract features relating to the texture translation priors  802 , the corrected segmentation mask  608 , and the fine warped product digital image  322 . The virtual try-on digital image generation system  102  further pass these features through an upsampling convolutional decoder  808  (and/or other components/layers) to generate two outputs—an RGB rendered person image  812  and a composition mask  810 . For example, the neural network  804  produces a 4-channel output where three channels are the R, G, and B values of the rendered person image  812 , and the fourth channel is the composite mask  810 . 
     Using these two outputs, the virtual try-on digital image generation system  102  further generates the virtual try-on digital image  814  by combining the composition mask  810 , the rendered person image  812 , and the fine warped product digital image  322 . For example, the virtual try-on digital image generation system  102  generates the virtual try-on digital image  814  in accordance with:
 
 I   try-on   =M   cm   *I   stn   1 +(1− M   cm )* I   rp  
 
where I try-on  represents the virtual try-on digital image  814 , M cm  represents the composite mask  810 , I stn   1  represents the fine warped product digital image  322 , and I rp  represents the RGB rendered person image  812 . Because the virtual try-on digital image generation system  102  utilizes the unaffected parts of the model digital image  302  as a prior (e.g., the texture translation priors  802 ), the virtual try-on digital image generation system  102  is able to better translate texture of auxiliary (i.e., non-replaced) products such as pants/bottoms onto the virtual try-on digital image  814 .
 
     To help ensure that the neural network  804  generates accurate virtual try-on digital images (e.g., the virtual try-on digital image  814 ), the virtual try-on digital image generation system  102  can train the neural network  804 .  FIG. 9  illustrates training the neural network  804  in accordance with one or more embodiments. As shown, the virtual try-on digital image generation system  102  accesses training data such as a fine warped product digital image  902 , a corrected segmentation mask  904 , and a model digital image  906  (or texture translation priors of the model digital image  906 ) to input into the neural network  804 . Based on analyzing these three inputs, the neural network  804  generates a predicted composite mask  908  and (as described above in relation to  FIG. 8 ) a predicted virtual try-on digital image  910  in accordance with the weights and parameters of the components and layers of the neural network  804 . 
     The virtual try-on digital image generation system  102  further performs a comparison  912  to compare the predicted composite mask  908 , the predicted virtual try-on digital image  910 , and a ground truth segmentation mask  710  (accessed from the database  406 ). In particular, the virtual try-on digital image generation system  102  performs the comparison  912  by utilizing one or more loss functions such as a texture translation loss function and/or a dueling triplet loss function. 
     To elaborate, the virtual try-on digital image generation system  102  can implement a texture transfer loss, which includes other loss components such as a perceptual distance loss and a mask loss. For example, the virtual try-on digital image generation system  102  can determine a texture transfer loss as given by:
 
 L   tt   =L   l1   +L   percep   +L   mask  
 
 L   l1   =|I   try-on   −I   m |
 
 I   percep   =|VGG ( I   try-on )− VGG ( I   m )|
 
 L   mask   =|M   cm   −M   gt   product |
 
where L tt  represents the texture transfer loss, L l1  represents an L I  distance loss, L percep  represents a perceptual distance loss, L mask  represents a mask loss, VGG (I try-on ) represents a VGG-19 feature space representation of the predicted virtual try-on digital image  910 , VGG(I m ) represents a VGG-19 feature space representation of the model digital image  906 , M cm  represents the predicted composite mask  908 , and M gt   product  is the binary mask representing the product worn by the model in the model digital image  906 .
 
     As shown, the virtual try-on digital image generation system  102  also performs back propagation  914  to modify weights or parameters associated with the neural network  804 . By modifying the weights/parameters, the virtual try-on digital image generation system  102  changes how the neural network  804  analyzes the inputs to generate outputs. Indeed, the virtual try-on digital image generation system  102  changes the interaction between various neurons or layers used for extracting features and determining relationships between features. Thus, upon multiple successive iterations or epochs of training with different inputs, repeating the comparison  912  and the back propagation  914  to continually modify the weights/parameters, the virtual try-on digital image generation system  102  reduces or minimizes the loss associated with the neural network  804  until it satisfies a threshold (and the neural network  804  therefore generates accurate predictions of composite masks and rendered person images). 
     In some embodiments, the virtual try-on digital image generation system  102  performs the training of  FIG. 9  in multiple phases. The first K steps of training are a conditioning phase whereby the virtual try-on digital image generation system  102  minimizes (or reduces) L tt  to produce reasonable results. In the subsequent phases (each lasting T steps), the virtual try-on digital image generation system  102  employs the L tt  augmented with a dueling triplet loss to fine-tune the results further. 
     For example, the virtual try-on digital image generation system  102  implements a dueling triplet loss (e.g., as part of the comparison  912 ) that is characterized by an anchor, a positive (with respect to the anchor), and a negative (with respect to the anchor). In using the dueling triplet loss, the virtual try-on digital image generation system  102  attempts to simultaneously push the anchor result toward the positive and away from the negative. To this end, the virtual try-on digital image generation system  102  pits the anchor (e.g., the output from the neural network  804  with the current weights) against the negative (e.g., the output from the neural network  804  with weights from the previous phase), and push it toward the positive (e.g., the ground truth). 
     As training progresses, this online hard negative training strategy helps the virtual try-on digital image generation system  102  push the results closer to the ground truth by updating the negative at discrete step intervals (T steps). In the fine-tuning phase, at step i (i&lt;K), the virtual try-on digital image generation system  102  determines the dueling triplet loss as: 
               i     p   ⁢   r   ⁢   e   ⁢   v       =     K   +     T   *     (       ⌊       i   -   K     T     ⌋     -   1     )                       D     n   ⁢   e   ⁢   g     i     =            I       t   ⁢   r   ⁢   y     ⁢   ­   ⁢     o   ⁢   n       i     -     I       t   ⁢   r   ⁢   y     -     o   ⁢   n         i     p   ⁢   r   ⁢   e   ⁢   v                              D     p   ⁢   o   ⁢   s     i     =            I       t   ⁢   r   ⁢   y     ⁢   ­   ⁢     o   ⁢   n       i     -     I   m                          L   d   i     =     max   ⁡     (         D     p   ⁢   o   ⁢   s     i     -     D   neg   i       ,   0     )             
where I try-on   i  is the virtual try-on digital image obtained from the neural network  804  with weights at the i th  iteration, and where L d   i  is the dueling triplet loss. In some embodiments, the virtual try-on digital image generation system  102  thus determines the overall loss for the neural network  804  as:
 
               L     try   -   on     i     =     {           L   tt           i   ≤   K                 L   tt     +     L   d   i             i   &gt;   K                   
where L try-on   i  is the overall loss for the neural network  804 .
 
     As mentioned, the virtual try-on digital image generation system  102  can generate virtual try-on digital images with greater accuracy than conventional digital image systems. Indeed, experimenters have demonstrated the improvements of the virtual try-on digital image generation system  102  as compared to conventional systems.  FIG. 10  illustrates an example table of some of the improvements of the virtual try-on digital image generation system  102  over conventional systems in accordance with one or more embodiments. 
     As shown in  FIG. 10 , the virtual try-on digital image generation system  102  utilizes a dataset for training and testing. In particular, the dataset can include 19,000 digital images of front-facing female models and corresponding upper-clothing isolated product digital images. There are 16,253 cleaned pairs which are split into a training set and a testing set of 14,221 and 2,032 pairs, respectively. The images in the testing set are rearranged into unpaired sets for qualitative evaluation and are kept paired for quantitative evaluation otherwise. In some embodiments, the virtual try-on digital image generation system  102  utilizes the dataset described by X. Han, Z. Wu, Z. Wu, R. Yu, and L. S. Davis in  VITON: An Image - Based Virtual Try - On Network , CoRR (2017), which is incorporated by reference herein in its entirety. 
     In the table of  FIG. 10 , the results of different variations of the virtual try-on digital image generation system  102  are represented by the notations GMM+SATT, C2F+SATT, and C2F+SATT-D (e.g., “SieveNet”), where SATT represents the texture translation network (of  FIGS. 6 and 8 ), C2F represents the coarse-to-fine warp network (of  FIG. 3 ), and SATT-D represents the texture translation network with the dueling triplet loss. The previous state-of-the-art system is represented by the name CP-VTON (as described by B. Wang, H. Zhang, X. Liang, Y. Chen, L. Lin, and M. Yang in Toward Characteristic-Preserving Image-based Virtual Try-on Network at CoRR, abs/1807.07688, Jul. 20, 2018. For comparing with the conventional CP-VTON system, all results of the table in  FIG. 10  are obtained using 4 NVIDIA 1080Ti on a computer with 16 GB of RAM. 
     As shown in  FIG. 10 , the virtual try-on digital image generation system  102  outperforms the conventional system in each of the five tabulated metrics. For example, the SieveNet version of the virtual try-on digital image generation system  102  exhibits a structural similarity (SSIM) of 0.766, a multi-scale structural similarity (MS-SSIM) of 0.809, a Frechet Inception Distance (FID) of 14.65, a peak signal to noise ratio (PSNR) of 16.98, and an inception score (IS) of 2.82±0.09. By comparison with conventional systems, the CP-VTON exhibits a structural similarity (SSIM) of 0.698, a multi-scale structural similarity (MS-SSIM) of 0.746, a Frechet Inception Distance (FID) of 20.331, a peak signal to noise ratio (PSNR) of 14.544, and an inception score (IS) of 2.66±0.14. The PSNR metric, for instance, illustrates that the quality of the output virtual try-on digital images of the virtual try-on digital image generation system  102  is higher (16.98) than the quality of the output from the CP-VTON system (14.544). 
     Based on this disclosure as well as the results of the table in  FIG. 10 , the virtual try-on digital image generation system  102  enjoys particular advantages over the CP-VTON system. Specifically, the virtual try-on digital image generation system  102  shows improve skin texture generation, better handling of occlusion, better handling of variation in poses, better avoidance of bleeding pixels/textures, better preservation of textures and patterns upon warping, and better geometric warping. Indeed, the comparison above in  FIGS. 2A-2B  clearly illustrates some of these advantages that are represented numerically in the table of  FIG. 10 , where  FIG. 2A  shows results of the CP-VTON system and  FIG. 2B  shows results of the virtual try-on digital image generation system  102 . 
     Looking now to  FIG. 11 , additional detail will be provided regarding components and capabilities of the virtual try-on digital image generation system  102 . Specifically,  FIG. 11  illustrates an example schematic diagram of the virtual try-on digital image generation system  102  on an example computing device  1100  (e.g., one or more of the client device  108  and/or the server(s)  104 ). As shown in  FIG. 11 , the virtual try-on digital image generation system  102  may include a coarse transformation manager  1102 , a fine transformation manager  1104 , a segmentation mask manager  1106 , a texture translation manager  1108 , and a storage manager  1110 . The storage manager  1110  can include one or more memory devices that store various data such as model digital images, product digital images, warped versions of a product digital images, neural networks, and/or warping parameters. 
     As just mentioned, the virtual try-on digital image generation system  102  includes a coarse transformation manager  1102 . In particular, the coarse transformation manager  1102  can manage, determine, generate, identify, or learn coarse transformation parameters. For example, the coarse transformation manager  1102  can utilize a coarse regression neural network to analyze a model digital image and a product digital image to determine coarse modifications to make to the product digital image to align with a pose and a shape of the model digital image. Thus, the coarse transformation manager  1102  can make coarse modifications to a product digital image to align with a model digital image. In some embodiments, the coarse transformation manager  1102  can train the coarse regression neural network to generate accurate coarse transformation parameters, as described above. The coarse transformation manager  1102  can also communicate with the storage manager  1110  to access digital images from, and to store coarse transformation parameters within, the database  1112  (e.g., the database  406 ). 
     In addition, the virtual try-on digital image generation system  102  includes a fine transformation manager  1104 . In particular, the fine transformation manager  1104  can manage, determine, generate, identify, or learn fine transformation parameters. For example, the fine transformation manager  1104  can utilize a fine regression neural network to analyze a model digital image and a product digital image to determine fine modifications to make to the product digital image to align with a pose and a shape of the model digital image. Thus, the fine transformation manager  1104  can make fine modifications to a product digital image to align with a model digital image. In some embodiments, together with the fine transformation manager  1104 , the fine transformation manager  1104  can train the fine regression neural network to generate accurate fine transformation parameters, as described above. The fine transformation manager  1104  can also communicate with the storage manager  1110  to access digital images from, and to store fine transformation parameters within, the database  1112 . 
     As shown, the virtual try-on digital image generation system  102  also includes a segmentation mask manager  1106 . In particular, the segmentation mask manager  1106  can manage, determine, generate, identify, or learn a corrected segmentation mask for a product digital image and a model digital image. For example, the segmentation mask manager  1106  can determine a segmentation mask associated with a model digital image and can further condition or correct the segmentation mask based on a product digital image to indicate a portion of the model digital image that is to be replaced by (warped) pixels of the product digital image. In some embodiments, the segmentation mask manager  1106  can train a neural network to generate accurate corrected segmentation masks. The segmentation mask manager  1106  can also communicate with the storage manager  1110  to store a corrected segmentation mask within the database  1112 . 
     Further, the virtual try-on digital image generation system  102  includes a texture translation manager  1108 . In particular, the texture translation manager  1108  can manage, determine, generate, implement, or learn a texture translation to translate a texture of a warped product digital image onto a model digital image. For example, the texture translation manager  1108  can replace pixels of a model digital image with pixels of a fine warped product digital image in accordance with a corrected segmentation mask (as accessed from the database  1112 ). Thus, the texture translation manager  1108  can generate a virtual try-on digital image based on a product digital image and a model digital image. In some embodiments, the texture translation manager  1108  can train a neural network to generate accurate virtual try-on digital images, as described above. 
     In one or more embodiments, each of the components of the virtual try-on digital image generation system  102  are in communication with one another using any suitable communication technologies. Additionally, the components of the virtual try-on digital image generation system  102  can be in communication with one or more other devices including one or more client devices described above. It will be recognized that although the components of the virtual try-on digital image generation system  102  are shown to be separate in  FIG. 11 , any of the subcomponents may be combined into fewer components, such as into a single component, or divided into more components as may serve a particular implementation. Furthermore, although the components of  FIG. 11  are described in connection with the virtual try-on digital image generation system  102 , at least some of the components for performing operations in conjunction with the virtual try-on digital image generation system  102  described herein may be implemented on other devices within the environment. 
     The components of the virtual try-on digital image generation system  102  can include software, hardware, or both. For example, the components of the virtual try-on digital image generation system  102  can include one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices (e.g., the computing device  1100 ). When executed by the one or more processors, the computer-executable instructions of the virtual try-on digital image generation system  102  can cause the computing device  1100  to perform the methods described herein. Alternatively, the components of the virtual try-on digital image generation system  102  can comprise hardware, such as a special purpose processing device to perform a certain function or group of functions. Additionally or alternatively, the components of the virtual try-on digital image generation system  102  can include a combination of computer-executable instructions and hardware. 
     Furthermore, the components of the virtual try-on digital image generation system  102  performing the functions described herein may, for example, be implemented as part of a stand-alone application, as a module of an application, as a plug-in for applications including content management applications, as a library function or functions that may be called by other applications, and/or as a cloud-computing model. Thus, the components of the virtual try-on digital image generation system  102  may be implemented as part of a stand-alone application on a personal computing device or a mobile device. Alternatively or additionally, the components of the virtual try-on digital image generation system  102  may be implemented in any application that allows creation and delivery of marketing content to users, including, but not limited to, applications in ADOBE EXPERIENCE MANAGER and ADOBE CREATIVE CLOUD, such as ADOBE PHOTO SHOP and ADOBE LIGHTROOM. “ADOBE,” “ADOBE EXPERIENCE MANAGER,” “ADOBE PHOTOSHOP,” “ADOBE LIGHTROOM,” and “ADOBE CREATIVE CLOUD” are trademarks of Adobe Inc. in the United States and/or other countries. 
       FIGS. 1-11 , the corresponding text, and the examples provide a number of different systems, methods, and non-transitory computer readable media for training a classification neural network to classify digital images in few-shot tasks based on self-supervision and manifold mixup. In addition to the foregoing, embodiments can also be described in terms of flowcharts comprising acts for accomplishing a particular result. For example,  FIGS. 12-13  illustrates flowcharts of example sequences of acts in accordance with one or more embodiments. 
     While  FIGS. 12-13  illustrate acts according to one embodiment, alternative embodiments may omit, add to, reorder, and/or modify any of the acts shown in  FIGS. 12-13 . The acts of  FIGS. 12-13  can be performed as part of a method. Alternatively, a non-transitory computer readable medium can comprise instructions, that when executed by one or more processors, cause a computing device to perform the acts of  FIGS. 12-13 . In still further embodiments, a system can perform the acts of  FIGS. 12-13 . Additionally, the acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or other similar acts. 
       FIG. 12  illustrates an example series of acts  1200  for generating a virtual try-on digital image based on transformation parameters. In particular, the series of acts  1200  includes an act  1202  of identifying a model digital image and a product digital image. 
     In addition, the series of acts  1200  includes an act  1204  of determining coarse transformation parameters. In particular, the act  1204  can include determining, based on the model digital image, coarse transformation parameters for transforming the product digital image to fit the model digital image. For example, the act  1204  can involve utilizing a coarse regression neural network to generate a coarse offset matrix. The coarse offset matrix can include coarse modifications for modifying portions of the product digital image to align with a pose and a shape of the model digital image 
     As shown, the series of acts  1200  includes an act  1206  of generating a coarse warped product digital image. In particular, the act  1206  can include generating a coarse warped product digital image by modifying the product digital image in accordance with the coarse transformation parameters. For example, the act  1206  can involve generating model digital image priors based on the model digital image and aligning the coarse warped product digital image with the model digital image based on the model digital image priors. In some embodiments, the act  1206  can involve determining a shape and a pose of the model digital image and modifying the product digital image by aligning portions of the product digital image with corresponding portions of the model digital image based on the shape and the pose of the model digital image. 
     Further, the series of acts  1200  includes an act  1208  of determining fire transformation parameters. In particular, the act  1208  can include determining, based on the coarse warped product digital image, fine transformation parameters for transforming the coarse warped product digital image to fit the model digital image. For example, the act  1208  can involve utilizing a fine regression neural network to generate a fine offset matrix. The fine offset matrix can include fine modifications for modifying portions of the coarse warped product digital image to align with a pose and a shape of the model digital image. 
     The series of acts  1200  also includes an act  1210  of generating a fine warped product digital image. In particular, the act  1210  can include generating a fine warped product digital image by modifying the product digital image in accordance with the coarse transformation parameters and the fine transformation parameters. 
     Additionally, the series of acts  1200  includes an act  1212  of generating a virtual try-on digital image. In particular, the act  1212  can include utilizing the fine warped product digital image to generate a virtual try-on digital image comprising a depiction of a model from the model digital image with pixels replaced by the warped product digital image such that the model appears to be wearing a product from the product digital image. A virtual try-on digital image can include a depiction of a model from the model digital image with pixels replaced by the warped product digital image such that the model appears to be wearing a product from the product digital image. 
       FIG. 13  illustrates an example series of acts  1300  for generating a virtual try-on digital image based on a corrected segmentation mask. In particular, the series of acts  1300  can include an act  1302  of generating a corrected segmentation mask. For example, the act  1302  can involve utilizing a first convolutional neural network to generate a corrected segmentation mask based on the product digital image and the model digital image. The act  1302  can include multiple acts such as an act  1304  of generating a segmentation mask based on priors and an act  1306  of correcting the segmentation mask based on a product digital image. 
     For example, the act  1304  can involve generating a segmentation mask based on digital image priors of the model digital image. Indeed, the act  1304  can involve identifying a first area of the model digital image that depicts pixels to be replaced by the product digital image, identifying a second area of the model digital image that depicts other pixels not to be replaced by the product digital image, and generating the segmentation mask depicting the first area different from the second area. 
     In addition, the act  1306  can include correcting the segmentation mask based on the product digital image to represent a mask of the product digital image. In particular, the act  1306  can involve modifying the first area depicted within the segmentation mask to cover pixels of the model digital image corresponding to pixels covered by the product digital image. 
     Additionally, the series of acts  1300  includes an act  1308  of generating a virtual try-on digital image. In particular, the act  1308  can involve generating a virtual try-on digital image depicting the fine warped product digital image fit onto the model digital image by utilizing a second convolutional neural network to combine the corrected segmentation mask and the fine warped product digital image. In some embodiments, the series of acts  1300  can include (either as part of the act  1308  or as separate acts) generating texture translation priors for the model digital image and generating the virtual try-on digital image by utilizing the second convolutional neural network to analyze the corrected segmentation mask, the fine warped product digital image, and the texture translation priors. In addition, the series of acts  1300  can include (either as part of the act  1308  or as a separate act) utilizing the second convolutional neural network to generate a composition mask and a rendered person image based on the corrected segmentation mask, the fine warped product digital image, and the texture translation priors. Additionally, the act  1308  can including generating the virtual try-on digital image by combining the composition mask, the rendered person image, and the fine warped product digital image. In some embodiments, the act  1308  can involve utilizing a coarse-to-fine warping process on the product digital image in relation to the model digital image. 
     The series of acts  1300  can also include an act of training the first convolutional neural network to generate the corrected segmentation mask based on a cross entropy loss. Additionally, the series of acts  1300  can include an act of training the second convolutional neural network to generate the virtual try-on digital image based on a texture transfer loss. The series of acts  1300  can also include an act of training the second convolutional neural network to generate the virtual try-on digital image based further on a dueling triplet loss. 
     In some embodiments, the series of acts  1200  and/or the series of acts  1300  can include an act of training one or more regression neural networks for use in performing the step for coarse-to-fine warping of the product digital image to align with the model digital image based on a perceptual geometric loss. Additionally, the series of acts  1200  and/or the series of acts  1300  can include an act of training one or more convolutional neural networks for use in performing the step for applying the texture transfer process to generate the virtual try-on digital image based on a texture transfer loss. Training the one or more convolutional neural networks can include determining a dueling triplet loss associated with the one or more convolutional neural networks. For example, the series of acts  1200  and/or the series of acts  1300  can include jointly training a coarse regression neural network for generating the coarse warped product digital image and a fine regression neural network for generating the fine warped product digital image based on a perceptual geometric loss. 
     Embodiments of the present disclosure may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory, as discussed in greater detail below. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. In particular, one or more of the processes described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., a memory, etc.), and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein. 
     Computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that store computer-executable instructions are non-transitory computer-readable storage media (devices). Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the disclosure can comprise at least two distinctly different kinds of computer-readable media: non-transitory computer-readable storage media (devices) and transmission media. 
     Non-transitory computer-readable storage media (devices) includes RAM, ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM), Flash memory, phase-change memory (“PCM”), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. 
     A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a transmission medium. Transmissions media can include a network and/or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. Combinations of the above should also be included within the scope of computer-readable media. 
     Further, upon reaching various computer system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to non-transitory computer-readable storage media (devices) (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and/or to less volatile computer storage media (devices) at a computer system. Thus, it should be understood that non-transitory computer-readable storage media (devices) can be included in computer system components that also (or even primarily) utilize transmission media. 
     Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor, cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. In some embodiments, computer-executable instructions are executed on a general-purpose computer to turn the general-purpose computer into a special purpose computer implementing elements of the disclosure. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims. 
     Those skilled in the art will appreciate that the disclosure may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices. 
     Embodiments of the present disclosure can also be implemented in cloud computing environments. In this description, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources. For example, cloud computing can be employed in the marketplace to offer ubiquitous and convenient on-demand access to the shared pool of configurable computing resources. The shared pool of configurable computing resources can be rapidly provisioned via virtualization and released with low management effort or service provider interaction, and then scaled accordingly. 
     A cloud-computing model can be composed of various characteristics such as, for example, on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, and so forth. A cloud-computing model can also expose various service models, such as, for example, Software as a Service (“SaaS”), Platform as a Service (“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computing model can also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, and so forth. In this description and in the claims, a “cloud-computing environment” is an environment in which cloud computing is employed. 
       FIG. 14  illustrates, in block diagram form, an example computing device  1400  (e.g., the computing device  1100 , the client device  108 , and/or the server(s)  104 ) that may be configured to perform one or more of the processes described above. One will appreciate that the virtual try-on digital image generation system  102  can comprise implementations of the computing device  1400 . As shown by  FIG. 14 , the computing device can comprise a processor  1402 , memory  1404 , a storage device  1406 , an I/O interface  1408 , and a communication interface  1410 . Furthermore, the computing device  1400  can include an input device such as a touchscreen, mouse, keyboard, etc. In certain embodiments, the computing device  1400  can include fewer or more components than those shown in  FIG. 14 . Components of computing device  1400  shown in  FIG. 14  will now be described in additional detail. 
     In particular embodiments, processor(s)  1402  includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, processor(s)  1402  may retrieve (or fetch) the instructions from an internal register, an internal cache, memory  1404 , or a storage device  1406  and decode and execute them. 
     The computing device  1400  includes memory  1404 , which is coupled to the processor(s)  1402 . The memory  1404  may be used for storing data, metadata, and programs for execution by the processor(s). The memory  1404  may include one or more of volatile and non-volatile memory devices, such as Random-Access Memory (“RAM”), Read Only Memory (“ROM”), a solid-state disk (“SSD”), Flash, Phase Change Memory (“PCM”), or other types of data storage. The memory  1404  may be internal or distributed memory. 
     The computing device  1400  includes a storage device  1406  includes storage for storing data or instructions. As an example, and not by way of limitation, storage device  1406  can comprise a non-transitory storage medium described above. The storage device  1406  may include a hard disk drive (HDD), flash memory, a Universal Serial Bus (USB) drive or a combination of these or other storage devices. 
     The computing device  1400  also includes one or more input or output (“I/O”) devices/interfaces  1408 , which are provided to allow a user to provide input to (such as user strokes), receive output from, and otherwise transfer data to and from the computing device  1400 . These I/O devices/interfaces  1408  may include a mouse, keypad or a keyboard, a touch screen, camera, optical scanner, network interface, modem, other known I/O devices or a combination of such I/O devices/interfaces  1408 . The touch screen may be activated with a writing device or a finger. 
     The I/O devices/interfaces  1408  may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain embodiments, devices/interfaces  1408  is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation. 
     The computing device  1400  can further include a communication interface  1410 . The communication interface  1410  can include hardware, software, or both. The communication interface  1410  can provide one or more interfaces for communication (such as, for example, packet-based communication) between the computing device and one or more other computing devices  1400  or one or more networks. As an example, and not by way of limitation, communication interface  1410  may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI. The computing device  1400  can further include a bus  1412 . The bus  1412  can comprise hardware, software, or both that couples components of computing device  1400  to each other. 
     In the foregoing specification, the invention has been described with reference to specific example embodiments thereof. Various embodiments and aspects of the invention(s) are described with reference to details discussed herein, and the accompanying drawings illustrate the various embodiments. The description above and drawings are illustrative of the invention and are not to be construed as limiting the invention. Numerous specific details are described to provide a thorough understanding of various embodiments of the present invention. 
     The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. For example, the methods described herein may be performed with less or more steps/acts or the steps/acts may be performed in differing orders. Additionally, the steps/acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or similar steps/acts. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.