Abstract
In this paper we present a hybrid approach to reconstruct hair dynamics from multi-view video sequences, captured under uncontrolled lighting conditions. The key of this method is a refinement approach that combines image-based reconstruction techniques with physically based hair simulation. Given an initially reconstructed sequence of hair fiber models, we develop a hair dynamics refinement system using particle-based simulation and incompressible fluid simulation. The system allows us to improve reconstructed hair fiber motions and complete missing fibers caused by occlusion or tracking failure. The refined space-time hair dynamics are consistent with video inputs and can be also used to generate novel hair animations of different hair styles. We validate this method through various real hair examples.
Huamin Wang
Chief Scientist
My research interests include physics-based simulation and modeling, generative AI, numerical analysis and nonlinear optimization.