![]() The canonical volume is processed by a 3D convolutional network G3D, and the driving volume v□→□ is orthographically projected into 2D features and processed by a 2D convolutional network G2D, which predicts an output image xˆ□→□. ![]() ![]() The first warping removes the source motion from the appearance features v□ by mapping them into a canonical coordinate space, and the second one imposes the driver motion. They are used to predict the 3D warpings w□→ and w→□ via the separate warping generators W□→ and W→□. These representations consist of the explicit head rotations R□/□, translations t□/□, and the latent expression descriptors z□/□. In parallel, we predict the motion representations from both the source and driving images using a motion encoder Emtn. To encode the appearance of the source frame, we predict volumetric features v□ and a global descriptor e□ from the source image via an appearance encoder Eapp. The system then turns the static image into a motion graphic where the head and face of the subject correspond to the movements of the driving image.įigure 2: Overview of our base model. It then applies to a static image, like a painted portrait or photograph. The neural architecture takes a supplied driving image, which is a video of a person making different facial expressions and movements. The team has developed convincing neural avatars of historical figures and even some modern celebrities. Using an animated driving image, the team has proposed a new set of neural architectures and training methods to deal with 'the particularly challenging task of cross-driving synthesis.' ![]() A group of researchers with Samsung Labs have developed improved neural head avatar technology to the megapixel resolution.
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