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2025 | OriginalPaper | Chapter

ZeST: Zero-Shot Material Transfer from a Single Image

Authors : Ta-Ying Cheng, Prafull Sharma, Andrew Markham, Niki Trigoni, Varun Jampani

Published in: Computer Vision – ECCV 2024

Publisher: Springer Nature Switzerland

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Abstract

The chapter introduces ZeST, a novel approach for zero-shot material transfer from a single image to another, addressing the challenges of 2D-to-2D material editing. Unlike previous methods that require explicit 3D geometry and illumination estimation, ZeST operates in the latent space of diffusion models, making it practical and widely applicable. The method combines geometry-guided generation and latent illumination guidance to transfer material appearances accurately. Extensive experiments and qualitative evaluations demonstrate ZeST's superior performance in photo-realism and material accuracy compared to various baselines. The chapter also highlights ZeST's potential applications, such as applying pre-designed materials to real-world images and performing lighting-aware material transfer. The method's simplicity and effectiveness make it a promising tool for graphic designers and AI researchers working on image generation and editing tasks.
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previous chapter SeFlow: A Self-supervised Scene Flow Method in Autonomous Driving
next chapter 3D Congealing: 3D-Aware Image Alignment in the Wild
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Metadata
Title
ZeST: Zero-Shot Material Transfer from a Single Image
Authors
Ta-Ying Cheng
Prafull Sharma
Andrew Markham
Niki Trigoni
Varun Jampani
Copyright Year
2025
Book
Computer Vision – ECCV 2024

Print ISBN: 978-3-031-73231-7

Electronic ISBN: 978-3-031-73232-4

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-73232-4_21

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