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Found 8 papers, 1 papers with code
TexTile: A Differentiable Metric for Texture Tileability
We introduce TexTile, a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i. e., the tileability).
NEnv: Neural Environment Maps for Global Illumination
We propose NEnv, a deep-learning fully-differentiable method, capable of compressing and learning to sample from a single environment map.
NeuBTF: Neural fields for BTF encoding and transfer
However, existing neural materials are immutable, meaning that their output for a certain query of UVs, camera, and light vector is fixed once they are trained.
UMat: Uncertainty-Aware Single Image High Resolution Material Capture
We showcase the performance of our method with a real dataset of digitized textile materials and show that a commodity flatbed scanner can produce the type of diffuse illumination required as input to our method.
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images
We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera.
SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps
We present SeamlessGAN, a method capable of automatically generating tileable texture maps from a single input exemplar.
A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond
Intrinsic imaging or intrinsic image decomposition has traditionally been described as the problem of decomposing an image into two layers: a reflectance, the albedo invariant color of the material; and a shading, produced by the interaction between light and geometry.
Neural Photometry-guided Visual Attribute Transfer
Our model relies on a supervised image-to-image translation framework and is agnostic to the transferred domain; we showcase a semantic segmentation, a normal map, and a stylization.
