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Precise representations of 3D faces are beneficial to various computer visionand graphics applications. Due to the data discretization and model linearity,however, it remains challenging to capture accurate identity and expressionclues in current studies. This paper presents a novel 3D morphable face model,namely ImFace, to learn a nonlinear and continuous space with implicit neuralrepresentations. It builds two explicitly disentangled deformation fields tomodel complex shapes associated with identities and expressions, respectively,and designs an improved learning strategy to extend embeddings of expressionsto allow more diverse changes. We further introduce a Neural Blend-Field tolearn sophisticated details by adaptively blending a series of local fields. Inaddition to ImFace, an effective preprocessing pipeline is proposed to addressthe issue of watertight input requirement in implicit representations, enablingthem to work with common facial surfaces for the first time. Extensiveexperiments are performed to demonstrate the superiority of ImFace.

ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations