Abstract: A system for generating avatars from user self-images is disclosed, whereby the system accesses a media content item of a user that includes a face of the user, analyzes data associated with the media content item using a first machine learning model to generate a first modified media content item, parses a portion of the first modified media content item corresponding to the face of the user, and analyzes data associated with the portion of the first modified media content item using a second machine learning model to generate a digital avatar for the user.

Abstract: A system for automating evidence collection for software compliance is disclosed herein. The system can include a user computer device, a plurality of repositories, a backend computer system. The backend computer system can store a collection engine comprising a large language model (“LLM”) that, when executed by a processor, causes the backend computer system to extract metadata from a software architecture diagram and metadata from repository information, map the software architecture diagram to the at least one repository of the plurality of repositories based on the extracted metadata, generate a prompt to retrieve evidence from the plurality of repositories based on the mapping, and retrieve evidence relevant to the at least one requirement from the plurality of repositories based on the prompt.

Abstract: Examples disclosed herein describe techniques for automatic image quality evaluation. A first set of images generated by a first automated image generator and a second set of images generated by a second automated image generator are accessed. A first machine learning model generates a first quality indicator for each image in the first set of images and the second set of images. A second machine learning model generates a second quality indicator for each image in the first set of images and the second set of images. Based on the generated indicators, a first image from the first set of images and a second image from the second set of images are automatically selected and compared. A first ranking of the first automated image generator and the second automated image generator is generated based on the comparison, and ranking data is caused to be presented on a device.

Abstract: Examples disclosed herein describe techniques for automatic image quality evaluation. A first set of images generated by a first automated image generator and a second set of images generated by a second automated image generator are accessed. A first machine learning model generates a first quality indicator for each image in the first set of images and the second set of images. A second machine learning model generates a second quality indicator for each image in the first set of images and the second set of images. Based on the generated indicators, a first image from the first set of images and a second image from the second set of images are automatically selected and compared. A first ranking of the first automated image generator and the second automated image generator is generated based on the comparison, and ranking data is caused to be presented on a device.

Abstract: A method of generating an image for use in a conversation taking place in a messaging application is disclosed. Conversation input text is received from a user of a portable device that includes a display. Model input text is generated from the conversation input text, which is processed with a text-to-image model to generate an image based on the model input text. The coordinates of a face in the image are determined, and the face of the user or another person is added to the image at the location. The final image is displayed on the portable device, and user input is received to transmit the image to a remote recipient.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an inpainted image of a replacement individual in the place of a reference individual in a reference image using a metadata comparison. In one aspect, a system comprises receiving a first image comprising one or more reference individuals and a second image comprising one or more replacement individuals; obtaining replacement metadata and reference metadata; determining, based on the obtained replacement metadata and reference metadata, at least one portion of the first image for replacing corresponding with the one or more reference individuals; and generating a third image that comprises a modification of the first image wherein the at least one portion of the first image is replaced with replacement content, the replacement content being generated based on (i) the one or more replacement individuals, (ii) the replacement metadata, and (iii) the reference metadata.

Abstract: Examples in the present disclosure relate to prompt-driven, conversation-specific image generation. A first user device of a first user of an interaction application transmits an image generation request. The interaction application enables the first user to interact with at least a second user. An image is automatically generated based on a prompt included in the image generation request, and the image is presented at the first user device. In response to receiving user input to select the image, the image is stored as a conversation-specific image in association with a first user profile of the first user and a second user profile of the second user. The conversation-specific image is presented at both the first user device of the first user and a second user device of the second user, together with conversation data associated with a conversation between the first user and the second user.

Abstract: Examples disclosed herein describe techniques related to automated image generation in an interaction system. An image generation request is received from a first user device associated with a first user of an interaction system. The image generation request comprises a text prompt. Responsive to receiving the image generation request, an image is automatically generated by an automated text-to-image generator, based on the text prompt. The image is caused to be presented on the first user device. An indication of user input to select the image is received from the user device. Responsive to receiving the indication of the user input to select the image, the image is associated with the first user within the interaction system, and a second user of the interaction system is enabled to be presented with the image.

Abstract: Examples disclosed herein describe prompt modification techniques for automated image generation. An image generation request comprising a base prompt is received from a user device. A plurality of prompt modifiers is identified. A processor-implemented scoring engine determines, for each prompt modifier, a modifier score. The modifier score for each prompt modifier is associated with the base prompt. One or more of the prompt modifiers are automatically selected based on the modifier scores. A modified prompt is generated. The modified prompt is based on the base prompt and the one or more selected prompt modifiers. The modified prompt is provided as input to an automated image generator to generate an image, and the image is caused to be presented on the user device.

Abstract: A system for generating avatars from user self-images is disclosed, whereby the system accesses a media content item of a user that includes a face of the user, analyzes data associated with the media content item using a first machine learning model to generate a first modified media content item, parses a portion of the first modified media content item corresponding to the face of the user, and analyzes data associated with the portion of the first modified media content item using a second machine learning model to generate a digital avatar for the user.

Abstract: A method of generating an image for use in a conversation taking place in a messaging application is disclosed. Conversation input text is received from a user of a portable device that includes a display. Model input text is generated from the conversation input text, which is processed with a text-to-image model to generate an image based on the model input text. The generated image is displayed on the portable device, and user input is received to transmit the image to a remote recipient.

Abstract: Examples disclosed herein describe aspect ratio conversion techniques for automated image generation. An image generation request comprising a prompt is received from a user device. A processor-implemented automated image generator may generate a first image based on the prompt. The first image has a first aspect ratio. According to some examples, a region of interest is determined in the first image, based on a prompt alignment indicator for the region of interest. The first image is then processed to obtain a second image. The processing includes an automatic cropping operation directed at the region of interest. The second image has a second aspect ratio that is different from the first aspect ratio. The second image is caused to be presented on the user device.

Abstract: Examples disclosed herein describe techniques related to automated image generation in an interaction system. An image generation request is received from a first user device associated with a first user of an interaction system. The image generation request comprises a text prompt. Responsive to receiving the image generation request, an image is automatically generated by an automated text-to-image generator, based on the text prompt. The image is caused to be presented on the first user device. An indication of user input to select the image is received from the user device. Responsive to receiving the indication of the user input to select the image, the image is associated with the first user within the interaction system, and a second user of the interaction system is enabled to be presented with the image.

Abstract: Examples disclosed herein describe techniques for automatic image quality evaluation. A first set of images generated by a first automated image generator and a second set of images generated by a second automated image generator are accessed. A first machine learning model generates a first quality indicator for each image in the first set of images and the second set of images. A second machine learning model generates a second quality indicator for each image in the first set of images and the second set of images. Based on the generated indicators, a first image from the first set of images and a second image from the second set of images are automatically selected and compared. A first ranking of the first automated image generator and the second automated image generator is generated based on the comparison, and ranking data is caused to be presented on a device.

Abstract: Examples disclosed herein describe prompt modification techniques for automated image generation. An image generation request comprising a base prompt is received from a user device. A plurality of prompt modifiers is identified. A processor-implemented scoring engine determines, for each prompt modifier, a modifier score. The modifier score for each prompt modifier is associated with the base prompt. One or more of the prompt modifiers are automatically selected based on the modifier scores. A modified prompt is generated. The modified prompt is based on the base prompt and the one or more selected prompt modifiers. The modified prompt is provided as input to an automated image generator to generate an image, and the image is caused to be presented on the user device.

Abstract: A method for detecting floor stains is disclosed. The method includes capturing images of a floor surface using one or more image capturing devices mounted on exterior top sides of the floor cleaning device body aimed in a forward drive direction. The images correspond to wide-angle view images. The method includes generating undistorted virtual top view image of the floor surface. The undistorted virtual top view image corresponds to a surround view image of the floor surface. The method includes detecting floor stain from undistorted virtual top view image using a first pre-trained machine learning model. The method includes processing floor stain to extract floor stain attribute. The floor stain attribute comprises at least one of: dimensions, type of floor stain, a distance of floor stain from image capturing devices, and a location of floor stain. The method includes cleaning floor stain based on the processing of the floor stain.

Abstract: A method of generating an image for use in a conversation taking place in a messaging application is disclosed. Conversation input text is received from a user of a portable device that includes a display. Model input text is generated from the conversation input text, which is processed with a text-to-image model to generate an image based on the model input text. The coordinates of a face in the image are determined, and the face of the user or another person is added to the image at the location. The final image is displayed on the portable device, and user input is received to transmit the image to a remote recipient.

Abstract: A method of generating an image for use in a conversation taking place in a messaging application is disclosed. Conversation input text is received from a user of a portable device that includes a display. Model input text is generated from the conversation input text, which is processed with a text-to-image model to generate an image based on the model input text. The generated image is displayed on the portable device, and user input is received to transmit the image to a remote recipient.

Abstract: A method of producing a grating structure comprises the steps of forming a stamp from flexible plastic material, the stamp including a negative of a periodic grating pattern on a first surface; forming an ink by applying a polymer film to the stamp, the ink including a first surface and an opposing second surface, wherein the first surface of the ink contacts the first surface of the stamp such that the ink retains a positive of the periodic grating pattern; placing the ink and the stamp on a substrate such that the second surface of the ink contacts an upper surface of the substrate; and removing the stamp from the ink by applying a tensional force to one edge of the stamp.

Abstract: A method of producing a grating structure comprises the steps of forming a stamp from flexible plastic material, the stamp including a negative of a periodic grating pattern on a first surface; forming an ink by applying a polymer film to the stamp, the ink including a first surface and an opposing second surface, wherein the first surface of the ink contacts the first surface of the stamp such that the ink retains a positive of the periodic grating pattern; placing the ink and the stamp on a substrate such that the second surface of the ink contacts an upper surface of the substrate; and removing the stamp from the ink by applying a tensional force to one edge of the stamp.