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Hair Modeling and Simulation by Style

CGFAcademic7 citesCFX

Reconstructs simulatable strands from artist-created hair meshes and applies style-specific simulation per cluster for efficiency.

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Abstract

As the deformation behaviors of hair strands vary greatly depending on the hairstyle, the computational cost and accuracy of hair movement simulations can be significantly improved by applying simulation methods specific to a certain style. This paper makes two contributions with regard to the simulation of various hair styles. First, we propose a novel method to reconstruct simulatable hair strands from hair meshes created by artists. Manually created hair meshes consist of numerous mesh patches, and the strand reconstruction process is challenged by the absence of connectivity information among the patches for the same strand and the omission of hidden parts of strands due to the manual creation process. To this end, we develop a two‐stage spectral clustering method for estimating the degree of connectivity among patches and a strand‐growing method that preserves hairstyles. Next, we develop a hairstyle classification method for style‐specific simulations. In particular, we propose a set of features for efficient classifications and show that classifiers trained with the proposed features have higher accuracy than those trained with naive features. Our method applies efficient simulation methods according to the hairstyle without specific user input, and thus is favorable for real‐time simulation.

How to read this

Category: Method: strand reconstruction from artist hair meshes plus style-specific simulation
Contributions: Reconstructs simulatable hair strands from artist-created hair meshes using two-stage spectral clustering plus a hairstyle-preserving strand-growing method
Proposes a hairstyle classification method with features that classify more accurately than naive features
Applies efficient style-specific simulation per cluster to improve cost and accuracy
Context: No explicit prior works are listed; it relates generally to hair-meshing/strand-reconstruction and physically based hair simulation, with the novel twist of selecting a simulation method based on classified hairstyle.
Correctness: Assumes mesh patches can be reliably grouped into strands despite missing connectivity and hidden geometry, and that style classification maps cleanly to an appropriate simulation method; reconstruction quality on messy or unusual artist meshes is the limitation to watch.
Clarity: Fairly accessible; a first pass conveys the two-contribution structure (reconstruction, then style-based simulation), and a second pass clarifies the clustering and feature definitions.
How to read it: Focus first on the strand-reconstruction pipeline, then on the classification features; second pass worthwhile mainly if you handle artist-authored hair meshes.

Related work

Artistic Simulation of Curly Hair 2013 / SCA
The Art and Technology of Hair Simulation in Disney's Moana 2017 / SIGGRAPH
Strand-Accurate Multi-View Hair Capture 2019 / CVPR
Hair Meshes 2009 / SIGGRAPH Asia

Keywords

hair simulation hairstyle classification strand reconstruction two-stage spectral clustering hair meshes braided hair wisp model guide strands mass-spring model altitude mass-spring model orientation field support vector machine random forest signed distance field strand growing hair-hair interaction style-specific simulation

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