  Tremendous efforts have been made to learn animatable and photorealistic
human avatars. Towards this end, both explicit and implicit 3D representations
are heavily studied for a holistic modeling and capture of the whole human
(e.g., body, clothing, face and hair), but neither representation is an optimal
choice in terms of representation efficacy since different parts of the human
avatar have different modeling desiderata. For example, meshes are generally
not suitable for modeling clothing and hair. Motivated by this, we present
Disentangled Avatars~(DELTA), which models humans with hybrid explicit-implicit
3D representations. DELTA takes a monocular RGB video as input, and produces a
human avatar with separate body and clothing/hair layers. Specifically, we
demonstrate two important applications for DELTA. For the first one, we
consider the disentanglement of the human body and clothing and in the second,
we disentangle the face and hair. To do so, DELTA represents the body or face
with an explicit mesh-based parametric 3D model and the clothing or hair with
an implicit neural radiance field. To make this possible, we design an
end-to-end differentiable renderer that integrates meshes into volumetric
rendering, enabling DELTA to learn directly from monocular videos without any
3D supervision. Finally, we show that how these two applications can be easily
combined to model full-body avatars, such that the hair, face, body and
clothing can be fully disentangled yet jointly rendered. Such a disentanglement
enables hair and clothing transfer to arbitrary body shapes. We empirically
validate the effectiveness of DELTA's disentanglement by demonstrating its
promising performance on disentangled reconstruction, virtual clothing try-on
and hairstyle transfer. To facilitate future research, we also release an
open-sourced pipeline for the study of hybrid human avatar modeling.

Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics