We propose a learning-based method to recover normals, specularity, and roughness from a
single diffuse image of a material, using microgeometry appearance as our primary cue …

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 …

Hand in hand with the rapid development of machine learning, deep learning and
generative AI algorithms and architectures, the graphics community has seen a remarkable …

Recently, single-image SVBRDF capture is formulated as a regression problem, which uses
a network to infer four SVBRDF maps from a flash-lit image. However, the accuracy is still …

Estimating spatially varying BRDF from a single image without complicated acquisition
devices is a challenging problem. In this paper, a deep learning based method was …

Estimating fabric mechanical properties is crucial to create realistic digital twins. Existing
methods typically require testing physical fabric samples with expensive devices or …

In this study, we present a new and innovative framework for acquiring high-quality SVBRDF
maps. Our approach addresses the limitations of the current methods and proposes a new …

This paper presents a novel neural material relighting method for revisualizing a photograph
of a planar spatially-varying material under novel viewing and lighting conditions. Our …

Recovering spatial-varying bi-directional reflectance distribution function (SVBRDF) from a
single hand-held captured image has been a meaningful but challenging task in computer …

Densely captured real-world materials require effective compression for rendering, material
generation and reconstruction. Neural networks with high compression rates and the ability …