Existing hair capture systems fail to produce strands that reflect the structures of real-world hairstyles. We introduce a system that reconstructs coherent and plausible wisps aware of the underlying hair structures from a set of still images without any special lighting. Our system first discovers locally coherent wisp structures in the reconstructed point cloud and the 3D orientation field, and then uses a novel graph data structure to reason about both the connectivity and directions of the local wisp structures in a global optimization. The wisps are then completed and used to synthesize hair strands which are robust against occlusion and missing data and plausible for animation and simulation. We show reconstruction results for a variety of complex hairstyles including curly, wispy, and messy hair.

@article{Luo:2013:SHC,
   author = "Linjie Luo and Hao Li and Szymon Rusinkiewicz",
   title = "Structure-Aware Hair Capture",
   journal = "ACM Transactions on Graphics (Proc. SIGGRAPH)",
   year = "2013",
   month = jul,
   volume = "32",
   number = "4"
}