  In physics-based cloth animation, rich folds and detailed wrinkles are
achieved at the cost of expensive computational resources and huge labor
tuning. Data-driven techniques make efforts to reduce the computation
significantly by a database. One type of methods relies on human poses to
synthesize fitted garments which cannot be applied to general cloth. Another
type of methods adds details to the coarse meshes without such restrictions.
However, existing works usually utilize coordinate-based representations which
cannot cope with large-scale deformation, and requires dense vertex
correspondences between coarse and fine meshes. Moreover, as such methods only
add details, they require coarse meshes to be close to fine meshes, which can
be either impossible, or require unrealistic constraints when generating fine
meshes. To address these challenges, we develop a temporally and spatially
as-consistent-as-possible deformation representation (named TS-ACAP) and a
DeformTransformer network to learn the mapping from low-resolution meshes to
detailed ones. This TS-ACAP representation is designed to ensure both spatial
and temporal consistency for sequential large-scale deformations from cloth
animations. With this representation, our DeformTransformer network first
utilizes two mesh-based encoders to extract the coarse and fine features,
respectively. To transduct the coarse features to the fine ones, we leverage
the Transformer network that consists of frame-level attention mechanisms to
ensure temporal coherence of the prediction. Experimental results show that our
method is able to produce reliable and realistic animations in various datasets
at high frame rates: 10 ~ 35 times faster than physics-based simulation, with
superior detail synthesis abilities than existing methods.
