We propose ZeST, a method for zero-shot material transfer to an object in the
input image given a material exemplar image. ZeST leverages existing diffusion
adapters to extract implicit material representation from the exemplar image.
This representation is used to transfer the material using pre-trained
inpainting diffusion model on the object in the input image using depth
estimates as geometry cue and grayscale object shading as illumination cues.
The method works on real images without any training resulting a zero-shot
approach. Both qualitative and quantitative results on real and synthetic
datasets demonstrate that ZeST outputs photorealistic images with transferred
materials. We also show the application of ZeST to perform multiple edits and
robust material assignment under different illuminations. Project Page:
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利用 ZeST 方法，基于材料示例图像从输入图像中提取隐式材料表示，然后利用预训练的修补扩散模型，在输入图像中使用深度估计作为几何线索和灰度对象着色作为照明线索，将材料转移到对象上，结果表现出无需训练的零样本方法，通过真实数据集和合成数据集的定性和定量结果证明 ZeST 可以输出具备材料转移的逼真图像。