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[bibtex]@InProceedings{Huang_2025_ICCV, author = {Huang, Nisha and Liu, Henglin and Lin, Yizhou and Huang, Kaer and Chen, Chubin and Guo, Jie and Lee, Tong-yee and Li, Xiu}, title = {MaTe: Images Are All You Need for Material Transfer via Diffusion Transformer}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2025}, pages = {15117-15126} }
MaTe: Images Are All You Need for Material Transfer via Diffusion Transformer
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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