Sparse Data Driven Mesh Deformation

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