GALA: Generating Animatable Layered Assets from a Single Scan
Taeksoo Kim Byungjun Kim Shunsuke Saito Hanbyul Joo
We present GALA, a framework that takes as input a single-layer clothed 3D
human mesh and decomposes it into complete multi-layered 3D assets. The outputs
can then be combined with other assets to create novel clothed human avatars
with any pose. Existing reconstruction approaches often treat clothed humans as
a single-layer of geometry and overlook the inherent compositionality of humans
with hairstyles, clothing, and accessories, thereby limiting the utility of the
meshes for downstream applications. Decomposing a single-layer mesh into
separate layers is a challenging task because it requires the synthesis of
plausible geometry and texture for the severely occluded regions. Moreover,
even with successful decomposition, meshes are not normalized in terms of poses
and body shapes, failing coherent composition with novel identities and poses.
To address these challenges, we propose to leverage the general knowledge of a
pretrained 2D diffusion model as geometry and appearance prior for humans and
other assets. We first separate the input mesh using the 3D surface
segmentation extracted from multi-view 2D segmentations. Then we synthesize the
missing geometry of different layers in both posed and canonical spaces using a
novel pose-guided Score Distillation Sampling (SDS) loss. Once we complete
inpainting high-fidelity 3D geometry, we also apply the same SDS loss to its
texture to obtain the complete appearance including the initially occluded
regions. Through a series of decomposition steps, we obtain multiple layers of
3D assets in a shared canonical space normalized in terms of poses and human
shapes, hence supporting effortless composition to novel identities and
reanimation with novel poses. Our experiments demonstrate the effectiveness of
our approach for decomposition, canonicalization, and composition tasks
compared to existing solutions.