Leveraging Intrinsic Properties for Non-Rigid Garment Alignment
Siyou Lin Boyao Zhou Zerong Zheng Hongwen Zhang Yebin Liu
We address the problem of aligning real-world 3D data of garments, which
benefits many applications such as texture learning, physical parameter
estimation, generative modeling of garments, etc. Existing extrinsic methods
typically perform non-rigid iterative closest point and struggle to align
details due to incorrect closest matches and rigidity constraints. While
intrinsic methods based on functional maps can produce high-quality
correspondences, they work under isometric assumptions and become unreliable
for garment deformations which are highly non-isometric. To achieve
wrinkle-level as well as texture-level alignment, we present a novel
coarse-to-fine two-stage method that leverages intrinsic manifold properties
with two neural deformation fields, in the 3D space and the intrinsic space,
respectively. The coarse stage performs a 3D fitting, where we leverage
intrinsic manifold properties to define a manifold deformation field. The
coarse fitting then induces a functional map that produces an alignment of
intrinsic embeddings. We further refine the intrinsic alignment with a second
neural deformation field for higher accuracy. We evaluate our method with our
captured garment dataset, GarmCap. The method achieves accurate wrinkle-level
and texture-level alignment and works for difficult garment types such as long
coats. Our project page is
https://jsnln.github.io/iccv2023_intrinsic/index.html.