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arXiv · 2605.15660

MaTe: Images Are All You Need for Material Transfer via Diffusion Transformer

Nisha Huang·Henglin Liu·Yizhou Lin·Kaer Huang·Chubin Chen·Jie Guo·Tong-Yee Lee·Xiu Li
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Abstract

Recent diffusion-based methods for material transfer rely on image fine-tuning or complex architectures with assistive networks, but face challenges including text dependency, extra computational costs, and feature misalignment. To address these limitations, we propose MaTe, a streamlined diffusion framework that eliminates textual guidance and reference networks. MaTe integrates input images at the token level, enabling unified processing via multi-modal attention in a shared latent space. This design removes the need for additional adapters, ControlNet, inversion sampling, or model fine-tuning. Extensive experiments demonstrate that MaTe achieves high-quality material generation under a zero-shot, training-free paradigm. It outperforms state-of-the-art methods in both visual quality and efficiency while preserving precise detail alignment, significantly simplifying inference prerequisites.

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Nisha Huang, Henglin Liu, Yizhou Lin, Kaer Huang, Chubin Chen, Jie Guo, Tong-Yee Lee, Xiu Li. 2026-05-15. MaTe: Images Are All You Need for Material Transfer via Diffusion Transformer. https://arxiv.org/abs/2605.15660

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