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The first warping removes the source motion from the appearance features v𝑠 by mapping them into a canonical coordinate space, and the second one imposes the driver motion. They are used to predict the 3D warpings w𝑠→ and w→𝑑 via the separate warping generators W𝑠→ and W→𝑑. These representations consist of the explicit head rotations R𝑠/𝑑, translations t𝑠/𝑑, and the latent expression descriptors z𝑠/𝑑. 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 v𝑠 and a global descriptor e𝑠 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.

Panasonic printer drivers