MatFusion: A Generative Diffusion Model for SVBRDF Capture

• URL: http://arxiv.org/abs/2406.06539v1
• Date: Wed, 24 Apr 2024 02:07:53 GMT
• Title: MatFusion: A Generative Diffusion Model for SVBRDF Capture
• Authors: Sam Sartor, Pieter Peers,
• Abstract summary: We formulate SVBRDF estimation from photographs as a diffusion task. We first train a novel unconditional SVBRDF diffusion backbone model on a set of 312,165 synthetic spatially varying material exemplars. We demonstrate the flexibility of our method by refining different SVBRDF diffusion models conditioned on different types of incident lighting.
• Score: 3.3090362820994526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations [21.411327264448058]
  We propose an expectation-maximization (EM) approach to train diffusion models from corrupted observations. Our method alternates between reconstructing clean images from corrupted data using a known diffusion model (E-step) and refining diffusion model weights based on these reconstructions (M-step) This iterative process leads the learned diffusion model to gradually converge to the true clean data distribution.
  arXiv  Detail & Related papers  (2024-07-01T07:00:17Z)
• Distilling Diffusion Models into Conditional GANs [90.76040478677609]
  We distill a complex multistep diffusion model into a single-step conditional GAN student model. For efficient regression loss, we propose E-LatentLPIPS, a perceptual loss operating directly in diffusion model's latent space. We demonstrate that our one-step generator outperforms cutting-edge one-step diffusion distillation models.
  arXiv  Detail & Related papers  (2024-05-09T17:59:40Z)
• ReflectanceFusion: Diffusion-based text to SVBRDF Generation [12.5036873986483]
  We introduce Reflectance Diffusion, a new neural text-to-texture model capable of generating high-fidelity SVBRDF maps from textual descriptions. Our method leverages a tandem neural approach, consisting of two modules, to accurately model the distribution of spatially varying reflectance.
  arXiv  Detail & Related papers  (2024-04-25T15:43:33Z)
• Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models trained on Corrupted Data [56.81246107125692]
  Ambient Diffusion Posterior Sampling (A-DPS) is a generative model pre-trained on one type of corruption. We show that A-DPS can sometimes outperform models trained on clean data for several image restoration tasks in both speed and performance. We extend the Ambient Diffusion framework to train MRI models with access only to Fourier subsampled multi-coil MRI measurements.
  arXiv  Detail & Related papers  (2024-03-13T17:28:20Z)
• Relightify: Relightable 3D Faces from a Single Image via Diffusion Models [86.3927548091627]
  We present the first approach to use diffusion models as a prior for highly accurate 3D facial BRDF reconstruction from a single image. In contrast to existing methods, we directly acquire the observed texture from the input image, thus, resulting in more faithful and consistent estimation.
  arXiv  Detail & Related papers  (2023-05-10T11:57:49Z)
• DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion [144.9653045465908]
  We propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM) Our approach yields promising fusion results in infrared-visible image fusion and medical image fusion.
  arXiv  Detail & Related papers  (2023-03-13T04:06:42Z)
• Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods [27.014858633903867]
  We present a training framework for feature disentanglement of Diffusion Models (FDiff) We propose two sampling methods that can boost the realism of our Diffusion Models and also enhance the controllability.
  arXiv  Detail & Related papers  (2023-02-28T07:43:00Z)
• SinDiffusion: Learning a Diffusion Model from a Single Natural Image [159.4285444680301]
  We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. It is based on two core designs. First, SinDiffusion is trained with a single model at a single scale instead of multiple models with progressive growing of scales. Second, we identify that a patch-level receptive field of the diffusion network is crucial and effective for capturing the image's patch statistics.
  arXiv  Detail & Related papers  (2022-11-22T18:00:03Z)
• Flexible Amortized Variational Inference in qBOLD MRI [56.4324135502282]
  Oxygen extraction fraction (OEF) and deoxygenated blood volume (DBV) are more ambiguously determined from the data. Existing inference methods tend to yield very noisy and underestimated OEF maps, while overestimating DBV. This work describes a novel probabilistic machine learning approach that can infer plausible distributions of OEF and DBV.
  arXiv  Detail & Related papers  (2022-03-11T10:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.