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We present a novel method to learn Personalized Implicit Neural Avatars(PINA) from a short RGB-D sequence. This allows non-expert users to create adetailed and personalized virtual copy of themselves, which can be animatedwith realistic clothing deformations. PINA does not require complete scans, nordoes it require a prior learned from large datasets of clothed humans. Learninga complete avatar in this setting is challenging, since only few depthobservations are available, which are noisy and incomplete (i.e. only partialvisibility of the body per frame). We propose a method to learn the shape andnon-rigid deformations via a pose-conditioned implicit surface and adeformation field, defined in canonical space. This allows us to fuse allpartial observations into a single consistent canonical representation. Fusionis formulated as a global optimization problem over the pose, shape andskinning parameters. The method can learn neural avatars from real noisy RGB-Dsequences for a diverse set of people and clothing styles and these avatars canbe animated given unseen motion sequences.

PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence