Abstract: Domain adaptation frameworks for producing a 3D avatar generative adversarial network (GAN) capable of generating an avatar based on a single photographic image. The 3D avatar GAN is produced by training a target domain using an artistic dataset. Each artistic dataset includes a plurality of source images, each associated with a style type, such as caricature, cartoon, and comic. The domain adaptation framework in some implementations starts with a source domain that has been trained according to a 3D GAN and a target domain trained with a 2D GAN. The framework fine-tunes the 2D GAN by training it with the artistic datasets. The resulting 3D avatar GAN generates a 3D artistic avatar and an editing module for performing semantic and geometric edits.

Abstract: Domain adaptation frameworks for producing a 3D avatar generative adversarial network (GAN) capable of generating an avatar based on a single photographic image. The 3D avatar GAN is produced by training a target domain using an artistic dataset. Each artistic dataset includes a plurality of source images, each associated with a style type, such as caricature, cartoon, and comic. The domain adaptation framework in some implementations starts with a source domain that has been trained according to a 3D GAN and a target domain trained with a 2D GAN. The framework fine-tunes the 2D GAN by training it with the artistic datasets. The resulting 3D avatar GAN generates a 3D artistic avatar and an editing module for performing semantic and geometric edits.

Abstract: Domain adaptation frameworks for producing a 3D avatar generative adversarial network (GAN) capable of generating an avatar based on a single photographic image. The 3D avatar GAN is produced by training a target domain using an artistic dataset. Each artistic dataset includes a plurality of source images, each associated with a style type, such as caricature, cartoon, and comic. The domain adaptation framework in some implementations starts with a source domain that has been trained according to a 3D GAN and a target domain trained with a 2D GAN. The framework fine-tunes the 2D GAN by training it with the artistic datasets. The resulting 3D avatar GAN generates a 3D artistic avatar and an editing module for performing semantic and geometric edits.

Abstract: A method, apparatus, and non-transitory computer readable medium for image processing are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include identifying an original image including a plurality of semantic attributes, wherein each of the semantic attributes represents a complex set of features of the original image; identifying a target attribute value that indicates a change to a target attribute of the semantic attributes; computing a modified feature vector based on the target attribute value, wherein the modified feature vector incorporates the change to the target attribute while holding at least one preserved attribute of the semantic attributes substantially unchanged; and generating a modified image based on the modified feature vector, wherein the modified image includes the change to the target attribute and retains the at least one preserved attribute from the original image.

Abstract: A method, apparatus, and non-transitory computer readable medium for image processing are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include identifying an original image including a plurality of semantic attributes, wherein each of the semantic attributes represents a complex set of features of the original image; identifying a target attribute value that indicates a change to a target attribute of the semantic attributes; computing a modified feature vector based on the target attribute value, wherein the modified feature vector incorporates the change to the target attribute while holding at least one preserved attribute of the semantic attributes substantially unchanged; and generating a modified image based on the modified feature vector, wherein the modified image includes the change to the target attribute and retains the at least one preserved attribute from the original image.