Abstract: An improved machine learning architecture is proposed that is adapted to generate mouth regions corresponding to a target audio track that can be used, for example, in lip dubbing a base video in a first language to match a second language in the target audio track. The proposed machine learning architecture specifically includes modifications to resolve an internal mouth ambiguity problem. A number of variants are proposed along with corresponding methods and computer program products/computer readable media.

Abstract: An image processing system comprising: a computer readable medium and at least one processor configured to provide a machine learning architecture for image processing. In particular, one or more modified keyframes are used for training the machine learning architecture. The modifications are then automatically propagated to remaining frames requiring modification through interpolation or extrapolation through processing remaining frames through the trained machine learning architecture. The generated modified frames or frame portions can then be inserted into an original video to generate a modified video where the modifications have been propagated. Example usages include automatic computational approaches for aging/de-aging and addition/removal of tattoos or other visual effects.

Abstract: A computer implemented system is described in various embodiments herein, the system includes a processor, a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to: receive an initial video data object V comprising a plurality of image frame data objects I; receive a set of time-synchronized viseme parameters corresponding to target audio data object A?; and process the initial video data object V and the set of time-synchronized viseme parameters using a machine learning network to generate an output video data object V?, wherein initial mouth regions in the initial video data object V have been replaced with replacement mouth regions generated based on the set of time-synchronized viseme parameters.

Abstract: An image processing system comprising: a computer readable medium and at least one processor configured to provide a machine learning architecture for image processing. In particular, modified keyframes are used for training the machine learning architecture. The modifications are then automatically propagated to remaining frames requiring modification through interpolation or extrapolation through processing remaining frames through the trained machine learning architecture. The generated modified frames or frame portions can then be inserted into an original video to generate a modified video where the modifications have been propagated. Example usages include automatic computational approaches for aging/de-aging and addition/removal of tattoos or other visual effects.

Abstract: A computer implemented system is described in various embodiments herein, the system includes a processor, a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to: receive an initial video data object V comprising a plurality of image frame data objects I; receive a set of time-synchronized viseme parameters corresponding to target audio data object A?; and process the initial video data object V and the set of time-synchronized viseme parameters using a machine learning network to generate an output video data object V?, wherein initial mouth regions in the initial video data object V have been replaced with replacement mouth regions generated based on the set of time-synchronized viseme parameters.

Abstract: An image processing system comprising: a computer readable medium and at least one processor configured to provide a machine learning architecture for image processing. In particular, modified keyframes are used for training the machine learning architecture. The modifications are then automatically propagated to remaining frames requiring modification through interpolation or extrapolation through processing remaining frames through the trained machine learning architecture. The generated modified frames or frame portions can then be inserted into an original video to generate a modified video where the modifications have been propagated. Example usages include automatic computational approaches for aging/de-aging and addition/removal of tattoos or other visual effects.

Abstract: An image processing system comprising: a computer readable medium and at least one processor configured to provide a machine learning architecture for image processing. In particular, keyframes are selected for modification by a visual artist, and the modifications are used for training the machine learning architecture. The modifications are then automatically propagated to remaining frames requiring modification through interpolation or extrapolation through processing remaining frames through the trained machine learning architecture. The generated modified frames or frame portions can then be inserted into an original video to generate a modified video where the modifications have been propagated. Example usages include automatic computational approaches for aging/de-aging and addition/removal of tattoos or other visual effects.

Abstract: An image processing system comprising: a computer readable medium and at least one processor configured to provide a machine learning architecture for image processing. In particular, keyframes are selected for modification by a visual artist, and the modifications are used for training the machine learning architecture. The modifications are then automatically propagated to remaining frames requiring modification through interpolation or extrapolation through processing remaining frames through the trained machine learning architecture. The generated modified frames or frame portions can then be inserted into an original video to generate a modified video where the modifications have been propagated. Example usages include automatic computational approaches for aging/de-aging and addition/removal of tattoos or other visual effects.