  Animatable clothing transfer, aiming at dressing and animating garments
across characters, is a challenging problem. Most human avatar works entangle
the representations of the human body and clothing together, which leads to
difficulties for virtual try-on across identities. What's worse, the entangled
representations usually fail to exactly track the sliding motion of garments.
To overcome these limitations, we present Layered Gaussian Avatars (LayGA), a
new representation that formulates body and clothing as two separate layers for
photorealistic animatable clothing transfer from multi-view videos. Our
representation is built upon the Gaussian map-based avatar for its excellent
representation power of garment details. However, the Gaussian map produces
unstructured 3D Gaussians distributed around the actual surface. The absence of
a smooth explicit surface raises challenges in accurate garment tracking and
collision handling between body and garments. Therefore, we propose two-stage
training involving single-layer reconstruction and multi-layer fitting. In the
single-layer reconstruction stage, we propose a series of geometric constraints
to reconstruct smooth surfaces and simultaneously obtain the segmentation
between body and clothing. Next, in the multi-layer fitting stage, we train two
separate models to represent body and clothing and utilize the reconstructed
clothing geometries as 3D supervision for more accurate garment tracking.
Furthermore, we propose geometry and rendering layers for both high-quality
geometric reconstruction and high-fidelity rendering. Overall, the proposed
LayGA realizes photorealistic animations and virtual try-on, and outperforms
other baseline methods. Our project page is
https://jsnln.github.io/layga/index.html.
