Title: Sparse Data Driven Mesh Deformation,
Abstract: Example-based mesh deformation methods are powerful tools for realistic shape
editing. However, existing techniques typically combine all the example
deformation modes, which can lead to overfitting, i.e. using a overly
complicated model to explain the user-specified deformation. This leads to
implausible or unstable deformation results, including unexpected global
changes outside the region of interest. To address this fundamental limitation,
we propose a sparse blending method that automatically selects a smaller number
of deformation modes to compactly describe the desired deformation. This along
with a suitably chosen deformation basis including spatially localized
deformation modes leads to significant advantages, including more meaningful,
reliable, and efficient deformations because fewer and localized deformation
modes are applied. To cope with large rotations, we develop a simple but
effective representation based on polar decomposition of deformation gradients,
which resolves the ambiguity of large global rotations using an
as-consistent-as-possible global optimization. This simple representation has a
closed form solution for derivatives, making it efficient for sparse localized
representation and thus ensuring interactive performance. Experimental results
show that our method outperforms state-of-the-art data-driven mesh deformation
methods, for both quality of results and efficiency.