Rendering 2025 - Symposium Track

Permanent URI for this collection

Now showing 1 - 1 of 1

A Controllable Appearance Representation for Flexible Transfer and Editing
(The Eurographics Association, 2025) Jimenez-Navarro, Santiago; Guerrero-Viu, Julia; Masia, Belen; Wang, Beibei; Wilkie, Alexander
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.