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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.
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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.
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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.