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pixel aligned volumetric avatars pva pixel aligned volumetric avatars amit raj 1 michael zollhöfer 2 tomas simon 2 jason saragih 2 shunsuke saito 2 james hays 1 stephen lombardi 2 1 georgia institute of technology 2 facebook reality labs paper abstract acquisition and rendering of photorealistic human heads is a highly challenging research problem of particular importance for virtual telepresence currently the highest quality is achieved by volumetric approaches trained in a person specific manner on multi view data these models better represent fine structure such as hair compared to simpler mesh based models volumetric models typically employ a global code to represent facial expressions such that they can be driven by a small set of animation parameters while such architectures achieve impressive rendering quality they can not easily be extended to the multi identity setting in this paper we devise a novel approach for predicting volumetric avatars of the human head given just a small number of inputs we enable generalization across identities by a novel parameterization that combines neural radiance fields with local pixel aligned features extracted directly from the inputs thus side stepping the need for very deep or complex networks our approach is trained in an end to end manner solely based on a photometric rerendering loss without requiring explicit 3d supervision we demonstrate that our approach outperforms the existing state of the art in terms of quality and is able to generate faithful facial expressions in a multi identity setting approach your browser does not support the video tag avatars with different expressions paper and supplementary material amit raj michael zollhöfer tomas simon jason saragih shunsuke saito james hays stephen lombardi pva pixel aligned volumetric avatars hosted on arxiv bibtex this template was originally made by phillip isola and richard zhang for a colorful eccv project