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Our overall pipeline consists of two major components, including a geometry network, which learns a deformable neural signed distance function (SDF) as the 3D face representation, and a rendering network, which learns to render on\u2010surface points of the neural SDF to match the input images via self\u2010supervised optimization. To handle in\u2010the\u2010wild sparse\u2010view input of the same target with different expressions at test time, we propose residual latent code to effectively expand the shape space of the learned implicit face representation as well as a novel view\u2010switch loss to enforce consistency among different views. 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