ControlMat: Controlled Generative Approach to Material Capture

Giuseppe Vecchio, Rosalie Martin, Arthur Roullier, Adrien Kaiser, Romain Rouffet, Valentin Deschaintre, Tamy Boubekeur
Adobe Research

Animated renderings of materials captured using ControlMat.


ControlMat teaser

Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks. We present ControlMat, a method which, given a single photograph with uncontrolled illumination as input, conditions a diffusion model to generate plausible, tileable, high-resolution physically-based digital materials. We carefully analyze the behavior of diffusion models for multi-channel outputs, adapt the sampling process to fuse multi-scale information and introduce rolled diffusion to enable both tileability and patched diffusion for high-resolution outputs. Our generative approach further permits exploration of a variety of materials which could correspond to the input image, mitigating the unknown lighting conditions. We show that our approach outperforms recent inference and latent-space-optimization methods, and carefully validate our diffusion process design choices.


ControlMat architecture

Overview of the ControlMat architecture. During training, the PBR maps are compressed into the latent representation $z$ using the encoder $\mathcal{E}$. Noise is then added to $z$ and the denoising is carried out by a U-Net model. The denoising process can be globally conditioned with the CLIP embedding of the prompt (text or image) and/or locally conditioned using the intermediate representation of a target photograph extracted by a ControlNet network. After $n$ denoising steps the new denoised latent vector $\hat{z}$ is projected back to pixel space using the decoder $\mathcal{D}$.

ControlMat banner


  title={ControlMat: Controlled Generative Approach to Material Capture},
  author={Vecchio, Giuseppe and Martin, Rosalie and Roullier, Arthur and Kaiser, Adrien and Rouffet, Romain and Deschaintre, Valentin and Boubekeur, Tamy},
  journal={arXiv preprint arXiv:2309.01700},