MatFusion: A Generative Diffusion Model for SVBRDF Capture
This addresses the challenge of material capture for computer graphics applications, offering a generative approach that allows user selection from multiple estimates, though it is incremental as it builds on diffusion models.
The paper tackles the problem of estimating spatially varying material properties (SVBRDF) from photographs by formulating it as a diffusion task, achieving equal or better accuracy than existing methods for a single photograph under colocated flash lighting.
We formulate SVBRDF estimation from photographs as a diffusion task. To model the distribution of spatially varying materials, we first train a novel unconditional SVBRDF diffusion backbone model on a large set of 312,165 synthetic spatially varying material exemplars. This SVBRDF diffusion backbone model, named MatFusion, can then serve as a basis for refining a conditional diffusion model to estimate the material properties from a photograph under controlled or uncontrolled lighting. Our backbone MatFusion model is trained using only a loss on the reflectance properties, and therefore refinement can be paired with more expensive rendering methods without the need for backpropagation during training. Because the conditional SVBRDF diffusion models are generative, we can synthesize multiple SVBRDF estimates from the same input photograph from which the user can select the one that best matches the users' expectation. We demonstrate the flexibility of our method by refining different SVBRDF diffusion models conditioned on different types of incident lighting, and show that for a single photograph under colocated flash lighting our method achieves equal or better accuracy than existing SVBRDF estimation methods.