Abstract: Current skeletonization algorithms strive to produce a single centered result which is homotopic and insensitive to surface noise. However, this traditional approach may not well capture the main parts of complex models, and may even produce poor results for applications such as animation. Instead, we approximate model topology through a target feature size $omega $, where undesired features smaller than $omega $ are smoothed, and features larger than $omega $ are retained into groups called bones. This relaxed feature-varying strategy allows applications to generate robust and meaningful results without requiring additional parameter tuning, even for damaged, noisy, complex, or high genus models.