Abstract: Systems, methods and non-transitory computer readable media for determining a degree of realism of an artificially generated visual content are provided. Artificially generated visual contents including a particular artificially generated visual content may be accessed. Captured visual contents may be accessed. For each person of a plurality of persons, a mix of visual contents including at least one artificially generated visual content and at least one captured visual content may be presented to the person, where the mix includes the particular artificially generated visual content. A reaction to the presentation indicative of whether the person believes that visual contents are artificially generated may be received. A degree of realism of the particular artificially generated visual content may be determined based on the reactions.

Abstract: Systems, methods and non-transitory computer readable media for inference based on different portions of a training set using a single inference model are provided. Textual inputs may be received, each of which may include a source-identifying-keyword. An inference model may be a result of training a machine learning model using a plurality of training examples. Each training example may include a respective textual content and a respective media content. The training examples may be grouped based on source-identifying-keywords included in the textual contents. Different parameters of the inference model may be based on different groups, and thereby be associated with different source-identifying-keywords. When generating new media content using the inference model and a textual input, parameters associated with the source-identifying-keyword included in the textual input may be used.

Abstract: Systems, methods and non-transitory computer readable media for generating content using a generative model without relying on selected training examples are provided. An input indicative of a desire to generate a new content using a generative model may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may be associated with a respective content. Further, an indication of a particular subgroup of at least one but not all of the plurality of training examples may be obtained. Based on the indication, the input and the generative model may be used to generate the new content, abstaining from basing the generation of the new content on any training example included in the particular subgroup. The new content may be provided.

Abstract: Systems, methods and non-transitory computer readable media for generating and orchestrating motion of visual contents are provided. A plurality of visual contents may be accessed. Data indicative of a layout of the plurality of visual contents in a user interface may be accessed. A sequence for the plurality of visual contents may be determined based on the layout. For each visual content of the plurality of visual contents, the visual content may be analyzed to generate a video clip including a motion of at least one object depicted in the visual content. A presentation of the plurality of visual contents in the user interface may be caused. The determined sequence for the plurality of visual contents may be used to orchestrate a series of playbacks of the generated video clips.

Abstract: Systems, methods and non-transitory computer readable media for propagating changes from one visual content to other visual contents are provided. A plurality of visual contents may be accessed. A first visual content and a modified version of the first visual content may be accessed. The first visual content and the modified version of the first visual content may be analyzed to determine a manipulation for the plurality of visual contents. The determined manipulation may be used to generate a manipulated visual content for each visual content of the plurality of visual contents. The generated manipulated visual contents may be provided.

Abstract: Systems, methods and non-transitory computer readable media for attributing generated audio contents to training examples are provided. A first audio content generated using a generative model may be received. The generative model may be a result of training a machine learning model using training examples. Each training example may be associated with a respective audio content. Properties of the first audio content may be determined. For each training example of the training examples, the respective audio content may be analyzed to determine properties of the respective audio content. The properties of the first audio content and the properties of the audio contents associated with the training examples may be used to attribute the first audio content to a subgroup of the training examples. A respective data-record associated with a source associated with the training examples of the subgroup may be updated based on the attribution.

Abstract: Systems, methods and non-transitory computer readable media for attributing aspects of generated visual contents to training examples are provided. A first visual content generated using a generative model may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Properties of an aspect of the first visual content and properties of visual contents associated with the plurality of training examples may be used to attribute the aspect of the first visual content to a subgroup of the plurality of training examples. For each source of the sources associated with the visual contents associated with the training examples of the subgroup, a data-record associated with the source may be updated based on the attribution of the aspect of the first visual content.

Abstract: Systems, methods and non-transitory computer readable media for prompt-based attribution of generated media contents to training examples are provided. In some examples, a first media content generated using a generative model in response to a first textual input may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may include a respective textual content and a respective media content. Properties of the first textual input and properties of the textual contents included in the plurality of training examples may be used to attribute the first media content to a first subgroup of the plurality of training examples. The training examples of the first subgroup may be associated with a source. Further, a data-record associated with the source may be updated based on the attribution.

Abstract: Systems, methods and non-transitory computer readable media for identifying prompts used for training of inference models are provided. In some examples, a specific textual prompt in a natural language may be received. Further, data based on at least one parameter of an inference model may be accessed. The inference model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may include a respective textual content and a respective media content. The data and the specific textual prompt may be analyzed to determine a likelihood that the specific textual prompt is included in at least one training example of the plurality of training examples. A digital signal indicative of the likelihood that the specific textual prompt is included in at least one training example of the plurality of training examples may be generated.

Abstract: Systems, methods and non-transitory computer readable media for optimizing visual contents are provided. A particular mathematical object corresponding to a particular visual content in a mathematical space including a plurality of mathematical objects corresponding to visual contents may be determined. The mathematical space and the particular mathematical object may be used to obtain first and second mathematical objects of the plurality of mathematical objects. The visual content corresponding to the first mathematical object may be used in a communication with a first user and the visual content corresponding to the second mathematical object may be used in a communication with a second user. Indications of the reactions of the first and second users to the communications may be received. A third visual content may be obtained based on the reactions. The third visual content may be used in a communication with a third user.

Abstract: Systems, methods and non-transitory computer readable media for generating and modifying synthetic visual content using textual input are provided. One or more keywords may be received from a user. The one or more keywords may be used to generate a plurality of textual descriptions. Each generated textual description may correspond to a possible visual content. The generated plurality of textual descriptions may be presented to the user through a user interface that enables the user to modify the presented textual descriptions. A modification to at least one of the plurality of textual descriptions may be received from the user, therefore obtaining a modified plurality of textual descriptions. A selection of one textual description of the modified plurality of textual descriptions may be received from the user. A plurality of visual contents corresponding to the selected textual description may be presented to the user.

Abstract: Systems, methods and non-transitory computer readable media for generating visual content consistent with aspects of a visual brand language are provided. An indication of at least one aspect of a visual brand language may be received. Further, an indication of a desired visual content may be received. A new visual content consistent with the visual brand language and corresponding to the desired visual content may be generated based on the indication of the at least one aspect of the visual brand language and the indication of the desired visual content. The new visual content may be provided in a format ready for presentation.

Abstract: Systems, methods and non-transitory computer readable media for generating looped video clips are provided. A still image may be received. The still image may be analyzed to generate a series of images. The series of images may include at least first, middle and last images. The first image may be substantially visually similar to the last image, and the middle image may be visually different from the first and last images. The series of images may be provided. Playing the series of images in a video clip that starts with the first image and finishes with the last image, and repeating the video clip from the first image immediately after completing the playing of the video clip with the last image may create visually smooth transaction in which the transition from the last image to the first image is visually indistinguishable from the transactions between frames within the video clip.