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Capturing and rendering life-like hair is particularly challenging due to itsfine geometric structure, the complex physical interaction and its non-trivialvisual appearance.Yet, hair is a critical component for believable avatars. Inthis paper, we address the aforementioned problems: 1) we use a novel,volumetric hair representation that is com-posed of thousands of primitives.Each primitive can be rendered efficiently, yet realistically, by building onthe latest advances in neural rendering. 2) To have a reliable control signal,we present a novel way of tracking hair on the strand level. To keep thecomputational effort manageable, we use guide hairs and classic techniques toexpand those into a dense hood of hair. 3) To better enforce temporalconsistency and generalization ability of our model, we further optimize the 3Dscene flow of our representation with multi-view optical flow, using volumetricray marching. Our method can not only create realistic renders of recordedmulti-view sequences, but also create renderings for new hair configurations byproviding new control signals. We compare our method with existing work onviewpoint synthesis and drivable animation and achieve state-of-the-artresults. Please check out our project website athttps://ziyanw1.github.io/hvh/.

HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture