Style Brush: Guided Style Transfer for 3D Objects

Abstract We introduce Style Brush, a novel style transfer method for textured meshes designed to empower artists with fine‐grained control over the stylization process. Our approach extends traditional 3D style transfer methods by introducing a novel loss function that captures style directionality, supports multiple style images or portions thereof and enables smooth transitions between styles in the synthesized texture. The use of easily generated guiding textures streamlines user interaction, making our approach accessible to a broad audience. Extensive evaluations with various meshes, style images and contour shapes demonstrate the flexibility of our method and showcase the visual appeal of the generated textures. Finally, the results of a user study indicate that our approach generates visually appealing mesh textures that adhere to user‐defined guidance and enable users to retain creative control during stylization. Our implementation is available on: https://github.com/AronKovacs/style‐brush .

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Publication Details

Journal
Computer Graphics Forum
Published
2026-02-16
DOI
https://doi.org/10.1111/cgf.70308
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
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article

Style Brush: Guided Style Transfer for 3D Objects

Renata G. Raidou, Á. Kovács, Pedro Hermosilla
Computer Graphics Forum
Generative Adversarial Networks and Image Synthesis
article

Style Brush: Guided Style Transfer for 3D Objects

Renata G. Raidou, Á. Kovács, Pedro Hermosilla
article en

Abstract

Abstract We introduce Style Brush, a novel style transfer method for textured meshes designed to empower artists with fine‐grained control over the stylization process. Our approach extends traditional 3D style transfer methods by introducing a novel loss function that captures style directionality, supports multiple style images or portions thereof and enables smooth transitions between styles in the synthesized texture. The use of easily generated guiding textures streamlines user interaction, making our approach accessible to a broad audience. Extensive evaluations with various meshes, style images and contour shapes demonstrate the flexibility of our method and showcase the visual appeal of the generated textures. Finally, the results of a user study indicate that our approach generates visually appealing mesh textures that adhere to user‐defined guidance and enable users to retain creative control during stylization. Our implementation is available on: https://github.com/AronKovacs/style‐brush .

Computer Graphics Forum
TU Wien (AT)
Openalex Percentile: Top 55%
Generative Adversarial Networks and Image Synthesis
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