Abstract: The subject technology trains a neural network based on a training process. The subject technology selects a frame from an input video, the selected frame comprising image data including a representation of a face and hair, the representation of the hair being masked. The subject technology determines a previous predicted frame. The subject technology concatenates the selected frame and the previous predicted frame to generate a concatenated frame, the concatenated frame being provided to the neural network. The subject technology generates, using the neural network, a set of outputs including an output tensor, warping field, and a soft mask. The subject technology performs, using a warping field, a warp of the selected frame and the output tensor. The subject technology generates a prediction corresponding to a corrected texture rendering of the selected frame.

Abstract: Examples described herein relate to techniques for facilitating selection of stickers for inclusion in messages within the context of an interaction system. According to some examples, message content is detected and a set of candidate stickers is identified based on the message content. A search icon is dynamically replaced with a representation of respective ones of the set of candidate stickers. At a first point in time, the search icon represents a first candidate sticker of the set of candidate stickers. At a second point in time, the search icon represents a second candidate sticker of the set of candidate stickers.

Abstract: A mobile application with an improved user interface facilitates generating stylized media content items including images and videos. An end-user selects a desired visual effect from a set of options. The mobile application captures or accesses an image. The image is processed on a server using a generative neural network pre-trained to apply stylizations based on the selected effect. The server sends back the stylized image to the mobile application for display. The end-user can then save the stylized image or generate a video (e.g., an animation) showing the original image transition to the stylized image. The user interface provides an efficient creative workflow to apply aesthetic enhancements in a visual style chosen by the end-user. Generative machine learning techniques automate stylization to enable accessible media customization and sharing.

Abstract: Examples described herein relate to techniques for facilitating selection of stickers for inclusion in messages within the context of an interaction system. According to some examples, sticker selection history data of a user of an interaction application is retrieved. Absence of a sticker suggestion trigger with respect to a messaging interface of the interaction application is detected. Presence of the sticker suggestion trigger results in presentation of a suggested sticker for transmission via the messaging interface. Based on the sticker selection history data and the absence of the sticker suggestion trigger, a sticker search icon state is selected. A search icon corresponding to the sticker search icon state is caused to be presented within the messaging interface.

Abstract: Examples described herein relate to techniques for facilitating selection of stickers for inclusion in messages within the context of an interaction system. According to some examples, message content is detected and a set of candidate stickers is identified based on the message content. A search icon is dynamically replaced with a representation of respective ones of the set of candidate stickers. At a first point in time, the search icon represents a first candidate sticker of the set of candidate stickers. At a second point in time, the search icon represents a second candidate sticker of the set of candidate stickers.

Abstract: A method includes determining participation in an interaction function by a first user of an interaction system with a second user of the interaction system. The method also includes accessing profile data of the first user, and determining, based on the profile data, whether the first user has captured or designated a first-user self-image for use in the interaction function. In response to determining that the first user has not captured or designated the first-user self-image, the method includes accessing a media content item that includes a character, identifying a head portion of the character in the media content item, replacing the head portion with a placeholder space, and displaying the media content item with the placeholder space in a user interface corresponding to the interaction function.

Abstract: A method includes determining participation in an interaction function by a first user of an interaction system with a second user of the interaction system. The method also includes accessing profile data of the first user, and determining, based on the profile data, whether the first user has captured or designated a first-user self-image for use in the interaction function. In response to determining that the first user has not captured or designated the first-user self-image, the method includes accessing a media content item that includes a character, identifying a head portion of the character in the media content item, replacing the head portion with a placeholder space, and displaying the media content item with the placeholder space in a user interface corresponding to the interaction function.

Abstract: The subject technology generates a first image of a face using a GAN model. The subject technology applies 3D virtual hair on the first image to generate a second image with 3D virtual hair. The subject technology projects the second image with 3D virtual hair into a GAN latent space to generate a third image with realistic virtual hair. The subject technology performs a blend of the realistic virtual hair with the first image of the face to generate a new image with new realistic hair that corresponds to the 3D virtual hair. The subject technology trains a neural network that receives the second image with the 3D virtual hair and provides an output image with realistic virtual hair. The subject technology generates using the trained neural network, a particular output image with realistic hair based on a particular input image with 3D virtual hair.

Abstract: Embodiments enable virtual hair generation. The virtual hair generation can be performed by generating a first image of a face using a GAN model, applying 3D virtual hair on the first image to generate a second image with 3D virtual hair, projecting the second image with 3D virtual hair into a GAN latent space to generate a third image with virtual hair, performing a blend of the virtual hair with the first image of the face to generate a new image with new virtual hair that corresponds to the 3D virtual hair, training a neural network that receives the second image with the 3D virtual hair and provides an output image with virtual hair, and generating using the trained neural network, a particular output image with hair based on a particular input image with 3D virtual hair.

Abstract: The subject technology trains a neural network based on a training process. The subject technology selects a frame from an input video, the selected frame comprising image data including a representation of a face and hair, the representation of the hair being masked. The subject technology determines a previous predicted frame. The subject technology concatenates the selected frame and the previous predicted frame to generate a concatenated frame, the concatenated frame being provided to the neural network. The subject technology generates, using the neural network, a set of outputs including an output tensor, warping field, and a soft mask. The subject technology performs, using a warping field, a warp of the selected frame and the output tensor. The subject technology generates a prediction corresponding to a corrected texture rendering of the selected frame.

Abstract: A method includes determining participation in an interaction function by a first user of an interaction system with a second user of the interaction system. The method also includes accessing profile data of the first user, and determining, based on the profile data, whether the first user has captured or designated a first-user self-image for use in the interaction function. In response to determining that the first user has not captured or designated the first-user self-image, the method includes accessing a media content item that includes a character, identifying a head portion of the character in the media content item, replacing the head portion with a placeholder space, and displaying the media content item with the placeholder space in a user interface corresponding to the interaction function.

Abstract: Examples described herein relate to techniques for facilitating selection of stickers for inclusion in messages within the context of an interaction system. According to some examples, sticker selection history data of a user of an interaction application is retrieved. Absence of a sticker suggestion trigger with respect to a messaging interface of the interaction application is detected. Presence of the sticker suggestion trigger results in presentation of a suggested sticker for transmission via the messaging interface. Based on the sticker selection history data and the absence of the sticker suggestion trigger, a sticker search icon state is selected. A search icon corresponding to the sticker search icon state is caused to be presented within the messaging interface.

Abstract: Examples described herein relate to techniques for facilitating selection of stickers for inclusion in messages within the context of an interaction system. According to some examples, message content is detected and a set of candidate stickers is identified based on the message content. A search icon is dynamically replaced with a representation of respective ones of the set of candidate stickers. At a first point in time, the search icon represents a first candidate sticker of the set of candidate stickers. At a second point in time, the search icon represents a second candidate sticker of the set of candidate stickers.

Abstract: A mobile application with an improved user interface facilitates generating stylized media content items including images and videos. An end-user selects a desired visual effect from a set of options. The mobile application captures or accesses an image. The image is processed on a server using a generative neural network pre-trained to apply stylizations based on the selected effect. The server sends back the stylized image to the mobile application for display. The end-user can then save the stylized image or generate a video (e.g., an animation) showing the original image transition to the stylized image. The user interface provides an efficient creative workflow to apply aesthetic enhancements in a visual style chosen by the end-user. Generative machine learning techniques automate stylization to enable accessible media customization and sharing.

Abstract: A method includes determining participation in an interaction function by a first user of an interaction system with a second user of the interaction system. The method also includes accessing profile data of the first user, and determining, based on the profile data, whether the first user has captured or designated a first-user self-image for use in the interaction function. In response to determining that the first user has not captured or designated the first-user self-image, the method includes accessing a media content item that includes a character, identifying a head portion of the character in the media content item, replacing the head portion with a placeholder space, and displaying the media content item with the placeholder space in a user interface corresponding to the interaction function.

Abstract: The subject technology generates a first image of a face using a GAN model. The subject technology applies 3D virtual hair on the first image to generate a second image with 3D virtual hair. The subject technology projects the second image with 3D virtual hair into a GAN latent space to generate a third image with realistic virtual hair. The subject technology performs a blend of the realistic virtual hair with the first image of the face to generate a new image with new realistic hair that corresponds to the 3D virtual hair. The subject technology trains a neural network that receives the second image with the 3D virtual hair and provides an output image with realistic virtual hair. The subject technology generates using the trained neural network, a particular output image with realistic hair based on a particular input image with 3D virtual hair.