ZeST: Zero-Shot Material Transfer from a Single Image
Ta-Ying Cheng Prafull Sharma Andrew Markham Niki Trigoni Varun Jampani
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:
https://ttchengab.github.io/zest