  In the film and gaming industries, achieving a realistic hair appearance
typically involves the use of strands originating from the scalp. However,
reconstructing these strands from observed surface images of hair presents
significant challenges. The difficulty in acquiring Ground Truth (GT) data has
led state-of-the-art learning-based methods to rely on pre-training with
manually prepared synthetic CG data. This process is not only labor-intensive
and costly but also introduces complications due to the domain gap when
compared to real-world data. In this study, we propose an optimization-based
approach that eliminates the need for pre-training. Our method represents hair
strands as line segments growing from the scalp and optimizes them using a
novel differentiable rendering algorithm. To robustly optimize a substantial
number of slender explicit geometries, we introduce 3D orientation estimation
utilizing global optimization, strand initialization based on Laplace's
equation, and reparameterization that leverages geometric connectivity and
spatial proximity. Unlike existing optimization-based methods, our method is
capable of reconstructing internal hair flow in an absolute direction. Our
method exhibits robust and accurate inverse rendering, surpassing the quality
of existing methods and significantly improving processing speed.
