Abstract: A system and method for concealing and revealing a human face in media objects may include obtaining a first media object capturing an image of the face; employing a first unit to: extract, from the first media object, a set of features representing the face, and generate a second media object, by embedding the extracted features in an anonymized media object; and employing a second unit to recognize the face based on the second media object.

Abstract: A system and method for de-identification of an image may include receiving an input image of a human face; iteratively modifying the input image to produce an output image of the human face until: (a) a threshold indicating how unlikely a human is to associate the input image with the output image is reached, and (b) a threshold indicating an ability of a computerized unit to associate the input image with the output image is reached; and providing the output image.

Abstract: A system and a method of performing face recognition may include: receiving a first facial image, depicting a first face, and a second facial image depicting a second face; applying an ML model on the first image, to produce a first representation vector, and applying the ML model on the second image to produce a second representation vector; comparing the first representation vector and the second representation vector; and associating the first face with the second face based on the comparison, where the ML model is trained to produce the representation vectors from the facial images, based on regions in the facial images that correspond to distinctiveness scores that are beneath a distinctiveness threshold.

Abstract: To consistently anonymize video, an identity-preserving encoder may be trained with a decoder to preserve identity information unique to an individual in an encoded space. The identity-preserving encoder may encode source video images of a source identity to generate identity-preserving encoded data representing the source identity. A de-identification engine may generate de-identifying encoded data representing a different target identity. A transformation may be generated based on a difference between the identity-preserving and de-identifying encoded data, representing swapping the source identity with the target identity. The identity may be consistently swapped from the source to target in the encoded space by applying the same transformation to the identity-preserving encoded data for the source images.

Abstract: A system and method voice driven animation of an object in an image by sampling an input video, depicting a puppet object, to obtain an image, receiving audio data, extracting voice related features from the audio data, producing an expression representation based on the voice related features, wherein the expression representation is related to a region of interest, obtaining from the image, auxiliary data related to the image and generating a target image based on the expression representation and the auxiliary data.

Abstract: A system and method of animating an image of an object by extracting a plurality of three dimensional (3D) features from a first image, depicting a puppet object, and from the second image of a driver object, obtaining, a value of a first 3D identity-invariant feature of the puppet object, and a value of a second 3D identity-invariant feature, from the second image, calculating and applying, a mixing function, on the plurality of 3D features, generating a rendered image, based on the plurality of 3D features, the first 3D identity-invariant feature and the second 3D identity-invariant feature, and generating an output image based on the rendered image, the plurality of 3D features, the first image and the second image, wherein the output image depicting a target object comprising at least one 3D identity-invariant feature of the driver object.

Abstract: A system and method of performing facial image anonymization may include: obtaining, by at least one processor of a first computing device, at least one first image data element, depicting a first face; using, by the at least one processor, a first neural network (NN), to encode the facial image into a first low-dimension, latent vector; using, by the at least one processor, a transformation module, adapted to modify the first latent vector, to produce an anonymized latent vector; and transmitting, by the at least one processor, the anonymized latent vector, via a communication network to a remote second computing device, wherein the second computing device is adapted to decode the anonymized latent vector into a second image data element, depicting an anonymized face.

Abstract: A system and method of animating an image of an object may include: receiving a first image, depicting a “puppet” object; sampling an input video, depicting a second, “driver” object, to obtain at least one second image; obtaining, by a first machine-learning (ML) model, a first identity-invariant feature of the puppet object, from the first image; obtaining at least one second identity-invariant feature of the driver object, from the respective at least one second image; calculating, by a second ML model, a transformation function, based on the first identity-invariant feature and the at least one second identity-invariant feature; applying the calculated transformation function on the first image, to produce one or more third images, depicting a target object, including at least one identity-invariant feature of the driver object; and appending the one or more third images to produce an output video depicting animation of the puppet object.

Abstract: A system and method of animating an image of an object may include: receiving a first image, depicting a “puppet” object; sampling an input video, depicting a second, “driver” object, to obtain at least one second image; obtaining, by a first machine-learning (ML) model, a first identity-invariant feature of the puppet object, from the first image; obtaining at least one second identity-invariant feature of the driver object, from the respective at least one second image; calculating, by a second ML model, a transformation function, based on the first identity-invariant feature and the at least one second identity-invariant feature; applying the calculated transformation function on the first image, to produce one or more third images, depicting a target object, including at least one identity-invariant feature of the driver object; and appending the one or more third images to produce an output video depicting animation of the puppet object.

Abstract: A system and method of animating an image of an object by extracting a plurality of three dimensional (3D) features from a first image, depicting a puppet object, and from the second image of a driver object, obtaining, a value of a first 3D identity-invariant feature of the puppet object, and a value of a second 3D identity-invariant feature, from the second image, calculating and applying, a mixing function, on the plurality of 3D features, generating a rendered image, based on the plurality of 3D features, the first 3D identity-invariant feature and the second 3D identity-invariant feature, and generating an output image based on the rendered image, the plurality of 3D features, the first image and the second image, wherein the output image depicting a target object comprising at least one 3D identity-invariant feature of the driver object.

Abstract: A system and method voice driven animation of an object in an image by sampling an input video, depicting a puppet object, to obtain an image, receiving audio data, extracting voice related features from the audio data, producing an expression representation based on the voice related features, wherein the expression representation is related to a region of interest, obtaining from the image, auxiliary data related to the image and generating a target image based on the expression representation and the auxiliary data.

Abstract: To consistently anonymize video, an identity-preserving encoder may be trained with a decoder to preserve identity information unique to an individual in an encoded space. The identity-preserving encoder may encode source video images of a source identity to generate identity-preserving encoded data representing the source identity. A de-identification engine may generate de-identifying encoded data representing a different target identity. A transformation may be generated based on a difference between the identity-preserving and de-identifying encoded data, representing swapping the source identity with the target identity. The identity may be consistently swapped from the source to target in the encoded space by applying the same transformation to the identity-preserving encoded data for the source images.