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Found 20 papers, 4 papers with code
Fine-Grained Spatially Varying Material Selection in Images
Selection is the first step in many image editing processes, enabling faster and simpler modifications of all pixels sharing a common modality.
DiffusedWrinkles: A Diffusion-Based Model for Data-Driven Garment Animation
We present a data-driven method for learning to generate animations of 3D garments using a 2D image diffusion model.
Single-image Reflectance and Transmittance Estimation from Any Flatbed Scanner
Flatbed scanners have emerged as promising devices for high-resolution, single-image material capture.
IReNe: Instant Recoloring of Neural Radiance Fields
Leveraging a pre-trained NeRF model and a single training image with user-applied color edits, IReNe swiftly adjusts network parameters in seconds.
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.
Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On
Then, after a mesh topology optimization step where we generate a sufficient level of detail for the input garment type, we further deform the mesh to reproduce deformations caused by the target body shape.
SoftSMPL: Data-driven Modeling of Nonlinear Soft-tissue Dynamics for Parametric Humans
We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion.
A Similarity Measure for Material Appearance
We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments.
Learning icons appearance similarity
Selecting an optimal set of icons is a crucial step in the pipeline of visual design to structure and navigate through content.
BRDF Estimation of Complex Materials with Nested Learning
The estimation of the optical properties of a material from RGB-images is an important but extremely ill-posed problem in Computer Graphics.
Convolutional sparse coding for capturing high speed video content
Video capture is limited by the trade-off between spatial and temporal resolution: when capturing videos of high temporal resolution, the spatial resolution decreases due to bandwidth limitations in the capture system.
Transfer Learning for Illustration Classification
In this paper, we use transfer learning techniques to propose a new classification network with better performance in illustration images.
Intrinsic Light Field Images
We present a method to automatically decompose a light field into its intrinsic shading and albedo components.
