no code implementations • 19 Mar 2024 • Carlos Rodriguez-Pardo, Dan Casas, Elena Garces, Jorge Lopez-Moreno
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).
1 code implementation • Computer Graphics Forum 2023 • Carlos Rodriguez-Pardo, Javier Fabre, Elena Garces, Jorge Lopez-Moreno
We propose NEnv, a deep-learning fully-differentiable method, capable of compressing and learning to sample from a single environment map.
no code implementations • 3 Jul 2023 • Carlos Rodriguez-Pardo, Konstantinos Kazatzis, Jorge Lopez-Moreno, Elena Garces
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.
no code implementations • CVPR 2023 • Carlos Rodriguez-Pardo, Henar Dominguez-Elvira, David Pascual-Hernandez, Elena Garces
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.
no code implementations • 13 Apr 2023 • Carlos Rodriguez-Pardo, Melania Prieto-Martin, Dan Casas, Elena Garces
We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera.
no code implementations • 13 Jan 2022 • Carlos Rodriguez-Pardo, Elena Garces
We present SeamlessGAN, a method capable of automatically generating tileable texture maps from a single input exemplar.
no code implementations • 7 Dec 2021 • Elena Garces, Carlos Rodriguez-Pardo, Dan Casas, Jorge Lopez-Moreno
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.
no code implementations • 5 Dec 2021 • Carlos Rodriguez-Pardo, Elena Garces
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.
no code implementations • 9 Sep 2020 • Raquel Vidaurre, Igor Santesteban, Elena Garces, Dan Casas
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.
no code implementations • 1 Apr 2020 • Igor Santesteban, Elena Garces, Miguel A. Otaduy, Dan Casas
We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion.
1 code implementation • 4 May 2019 • Manuel Lagunas, Sandra Malpica, Ana Serrano, Elena Garces, Diego Gutierrez, Belen Masia
We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments.
no code implementations • 1 Feb 2019 • Manuel Lagunas, Elena Garces, Diego Gutierrez
Selecting an optimal set of icons is a crucial step in the pipeline of visual design to structure and navigate through content.
no code implementations • 22 Nov 2018 • Raquel Vidaurre, Dan Casas, Elena Garces, Jorge Lopez-Moreno
The estimation of the optical properties of a material from RGB-images is an important but extremely ill-posed problem in Computer Graphics.
no code implementations • 13 Jun 2018 • Ana Serrano, Elena Garces, Diego Gutierrez, Belen Masia
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.
1 code implementation • 23 May 2018 • Manuel Lagunas, Elena Garces
In this paper, we use transfer learning techniques to propose a new classification network with better performance in illustration images.
no code implementations • 15 Aug 2016 • Elena Garces, Jose I. Echevarria, Wen Zhang, Hongzhi Wu, Kun Zhou, Diego Gutierrez
We present a method to automatically decompose a light field into its intrinsic shading and albedo components.