Transforming Unstructured Hair Strands into Procedural Hair Grooms

In recent years, reconstruction methods have been developed that can recover strand-level hair geometry from images. However, these methods recover a vast number of individual hair strands that are difficult to edit and simulate. Many methods also rely on neural priors to infer non-visible inner hair, which can result in poor inner hair structure for complex hairstyles, such as curly hair. We propose an inverse hair grooming pipeline that transforms the imperfect 3D strands from these reconstruction methods into procedural hair grooms that consist of a small set of guide strands and hair grooming operators, inspired by pipelines used by artists in popular 3D modeling tools such as Blender and Houdini. We take a probabilistic view of these hair grooms and design various optimization strategies and loss functions to optimize for the guide strands and operator parameters. Due to the proceduralism, our resulting grooms can naturally represent challenging hairstyles, have structurally sound inner hair, and are easily editable.