Shaping Strands with Neural Style Transfer

The intricate geometric complexity of knots, tangles, dreads and clumps require sophisticated grooming systems that allow artists to both realistically model and artistically control fur and hair systems. Recent volumetric and 3D neural style transfer techniques provided a new paradigm of art directability, allowing artists to modify assets drastically with the use of single style images. However, these previous 3D neural stylization approaches were limited to volumes and meshes. In this paper we propose the first stylization pipeline to support hair and fur. Through a carefully tailored fur/hair representation, our approach allows complex, 3D consistent and temporally coherent grooms that are stylized using style images.

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Publication Details

Journal
ACM Transactions on Graphics
Published
2025-12-01
DOI
https://doi.org/10.1145/3763365
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
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article

Shaping Strands with Neural Style Transfer

Peter Chang, Vinicius Azevedo, Jingwei Tang, Guilherme Gomes Haetinger et al.
ACM Transactions on Graphics
Generative Adversarial Networks and Image Synthesis
article

Shaping Strands with Neural Style Transfer

Peter Chang, Vinicius Azevedo, Jingwei Tang, Guilherme Gomes Haetinger, Beyzanur Coban
article en

Abstract

The intricate geometric complexity of knots, tangles, dreads and clumps require sophisticated grooming systems that allow artists to both realistically model and artistically control fur and hair systems. Recent volumetric and 3D neural style transfer techniques provided a new paradigm of art directability, allowing artists to modify assets drastically with the use of single style images. However, these previous 3D neural stylization approaches were limited to volumes and meshes. In this paper we propose the first stylization pipeline to support hair and fur. Through a carefully tailored fur/hair representation, our approach allows complex, 3D consistent and temporally coherent grooms that are stylized using style images.

ACM Transactions on GraphicsVol. 44(6)
ETH Zurich (CH), Walt Disney (Switzerland) (CH)
Openalex Percentile: Top 60%
Generative Adversarial Networks and Image Synthesis
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