Abstract: Techniques are disclosed for changing the identities of faces in images. In embodiments, a tunable model for changing facial identities in images includes an encoder, a decoder, and dense layers that generate either adaptive instance normalization (AdaIN) coefficients that control the operation of convolution layers in the decoder or the values of weights within such convolution layers, allowing the model to change the identity of a face in an image based on a user selection. A separate set of dense layers may be trained to generate AdaIN coefficients for each of a number of facial identities, and the AdaIN coefficients output by different sets of dense layers can be combined to interpolate between facial identities. Alternatively, a single set of dense layers may be trained to take as input an identity vector and output AdaIN coefficients or values of weighs within convolution layers of the decoder.

Abstract: Techniques are disclosed for changing the identities of faces in images. In embodiments, a tunable model for changing facial identities in images includes an encoder, a decoder, and dense layers that generate either adaptive instance normalization (AdaIN) coefficients that control the operation of convolution layers in the decoder or the values of weights within such convolution layers, allowing the model to change the identity of a face in an image based on a user selection. A separate set of dense layers may be trained to generate AdaIN coefficients for each of a number of facial identities, and the AdaIN coefficients output by different sets of dense layers can be combined to interpolate between facial identities. Alternatively, a single set of dense layers may be trained to take as input an identity vector and output AdaIN coefficients or values of weighs within convolution layers of the decoder.

Abstract: A vision assistance method implemented on a digital display device provides a test image for display to a user on a display of the graphical user interface of the device, and an adjustment interface, configured to be displayed adjacent to the test image on the display and enables the user to input visual adjustment settings to adjust the test image. The test image is then automatically adjusted by a processor of the device by applying the adjustment settings. A profile of the user is generated including the desired visual adjustment settings iteratively selected by the user. An input image is subsequently received and the processor then automatically adjusts the input image based upon visual adjustment data corresponding to the selected desired visual adjustment settings. The adjusted input image is then displayed on the display and to the user.