  Creating and customizing a 3D clothed avatar from textual descriptions is a
critical and challenging task. Traditional methods often treat the human body
and clothing as inseparable, limiting users' ability to freely mix and match
garments. In response to this limitation, we present LAyered Gaussian Avatar
(LAGA), a carefully designed framework enabling the creation of high-fidelity
decomposable avatars with diverse garments. By decoupling garments from avatar,
our framework empowers users to conviniently edit avatars at the garment level.
Our approach begins by modeling the avatar using a set of Gaussian points
organized in a layered structure, where each layer corresponds to a specific
garment or the human body itself. To generate high-quality garments for each
layer, we introduce a coarse-to-fine strategy for diverse garment generation
and a novel dual-SDS loss function to maintain coherence between the generated
garments and avatar components, including the human body and other garments.
Moreover, we introduce three regularization losses to guide the movement of
Gaussians for garment transfer, allowing garments to be freely transferred to
various avatars. Extensive experimentation demonstrates that our approach
surpasses existing methods in the generation of 3D clothed humans.
