A Controllable Appearance Representation for Flexible Transfer and Editing
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We present a method that computes an interpretable representation of material appearance within a highly compact, disentangled latent space. This representation is learned in a self-supervised fashion using a VAE-based model. We train our model with a carefully designed unlabeled dataset, avoiding possible biases induced by human-generated labels. Our model demonstrates strong disentanglement and interpretability by effectively encoding material appearance and illumination, despite the absence of explicit supervision. To showcase the capabilities of such a representation, we leverage it for two proof-of-concept applications: image-based appearance transfer and editing. Our representation is used to condition a diffusion pipeline that transfers the appearance of one or more images onto a target geometry, and allows the user to further edit the resulting appearance. This approach offers fine-grained control over the generated results: thanks to the well-structured compact latent space, users can intuitively manipulate attributes such as hue or glossiness in image space to achieve the desired final appearance.
Description
CCS Concepts: Computing methodologies -> Appearance and texture representations; Latent representations; material appearance; self-supervised learning
@inproceedings{10.2312:sr.20251187,
booktitle = {Eurographics Symposium on Rendering},
editor = {Wang, Beibei and Wilkie, Alexander},
title = {{A Controllable Appearance Representation for Flexible Transfer and Editing}},
author = {Jimenez-Navarro, Santiago and Guerrero-Viu, Julia and Masia, Belen},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {1727-3463},
ISBN = {978-3-03868-292-9},
DOI = {10.2312/sr.20251187}
}