ControlMat: A Controlled Generative Approach to Material Capture

• URL: http://arxiv.org/abs/2309.01700v3
• Date: Sat, 27 Jul 2024 16:20:40 GMT
• Title: ControlMat: A Controlled Generative Approach to Material Capture
• Authors: Giuseppe Vecchio, Rosalie Martin, Arthur Roullier, Adrien Kaiser, Romain Rouffet, Valentin Deschaintre, Tamy Boubekeur,
• Abstract summary: Material reconstruction from a photograph is a key component of 3D content creation democratization. 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.
• Score: 7.641962898125423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Supplemental materials and additional details are available at: https://gvecchio.com/controlmat/.

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