MARBLE utilizes material embeddings in CLIP-space to control pre-trained text-to-image models for blending and recomposing material properties in images with parametric control over attributes.
Editing materials of objects in images based on exemplar images is an active
area of research in computer vision and graphics. We propose MARBLE, a method
for performing material blending and recomposing fine-grained material
properties by finding material embeddings in CLIP-space and using that to
control pre-trained text-to-image models. We improve exemplar-based material
editing by finding a block in the denoising UNet responsible for material
attribution. Given two material exemplar-images, we find directions in the
CLIP-space for blending the materials. Further, we can achieve parametric
control over fine-grained material attributes such as roughness, metallic,
transparency, and glow using a shallow network to predict the direction for the
desired material attribute change. We perform qualitative and quantitative
analysis to demonstrate the efficacy of our proposed method. We also present
the ability of our method to perform multiple edits in a single forward pass
and applicability to painting.
Project Page: https://marblecontrol.github.io/