3D PixBrush: Image-Guided Local Texture Synthesis

We present 3D PixBrush, a method for performing image-driven edits of local regions on 3D meshes. 3D PixBrush predicts a localization mask and a synthesized texture that faithfully portray the object in the reference image. Our predicted localizations are both globally coherent and locally precise. Globally - our method contextualizes the object in the reference image and automatically positions it onto the input mesh. Locally - our method produces masks that conform to the geometry of the reference image. Notably, our method does not require any user input (in the form of scribbles or bounding boxes) to achieve accurate localizations. Instead, our method predicts a localization mask on the 3D mesh from scratch. To achieve this, we propose a modification to the score distillation sampling technique which incorporates both the predicted localization and the reference image, referred to as localization-modulated image guidance. We demonstrate the effectiveness of our proposed technique on a wide variety of meshes and images.

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Publication Details

Journal
ArXiv.org
Published
2025-07-04
Primary Topic
Generative Adversarial Networks and Image Synthesis
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article
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article

3D PixBrush: Image-Guided Local Texture Synthesis

Rana Hanocka, Dale Decatur, Kfir Aberman, Itai Lang
ArXiv.org
Generative Adversarial Networks and Image Synthesis
article

3D PixBrush: Image-Guided Local Texture Synthesis

Rana Hanocka, Dale Decatur, Kfir Aberman, Itai Lang
article en

Abstract

We present 3D PixBrush, a method for performing image-driven edits of local regions on 3D meshes. 3D PixBrush predicts a localization mask and a synthesized texture that faithfully portray the object in the reference image. Our predicted localizations are both globally coherent and locally precise. Globally - our method contextualizes the object in the reference image and automatically positions it onto the input mesh. Locally - our method produces masks that conform to the geometry of the reference image. Notably, our method does not require any user input (in the form of scribbles or bounding boxes) to achieve accurate localizations. Instead, our method predicts a localization mask on the 3D mesh from scratch. To achieve this, we propose a modification to the score distillation sampling technique which incorporates both the predicted localization and the reference image, referred to as localization-modulated image guidance. We demonstrate the effectiveness of our proposed technique on a wide variety of meshes and images.

ArXiv.org
Openalex Percentile: Top 71%
Generative Adversarial Networks and Image Synthesis
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3D PixBrush: Image-Guided Local Texture Synthesis — Rana Hanocka, Dale Decatur, et al. · ArXiv.org (2025) | TGRS Research Map | TGRS