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We have recently seen great progress in building photorealistic animatablefull-body codec avatars, but generating high-fidelity animation of clothing isstill difficult. To address these difficulties, we propose a method to build ananimatable clothed body avatar with an explicit representation of the clothingon the upper body from multi-view captured videos. We use a two-layer meshrepresentation to register each 3D scan separately with the body and clothingtemplates. In order to improve the photometric correspondence across differentframes, texture alignment is then performed through inverse rendering of theclothing geometry and texture predicted by a variational autoencoder. We thentrain a new two-layer codec avatar with separate modeling of the upper clothingand the inner body layer. To learn the interaction between the body dynamicsand clothing states, we use a temporal convolution network to predict theclothing latent code based on a sequence of input skeletal poses. We showphotorealistic animation output for three different actors, and demonstrate theadvantage of our clothed-body avatars over the single-layer avatars used inprevious work. We also show the benefit of an explicit clothing model thatallows the clothing texture to be edited in the animation output.

Modeling Clothing as a Separate Layer for an Animatable Human Avatar