1 code implementation • CVPR 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).
no code implementations • 4 Sep 2023 • Dan Casas, Marc Comino-Trinidad
We propose SMPLitex, a method for estimating and manipulating the complete 3D appearance of humans captured from a single image.
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 • 26 Oct 2022 • Andrés Casado-Elvira, Marc Comino Trinidad, Dan Casas
Clothing plays a fundamental role in digital humans.
no code implementations • 4 Oct 2022 • Jiayi Wang, Diogo Luvizon, Franziska Mueller, Florian Bernard, Adam Kortylewski, Dan Casas, Christian Theobalt
Through this, we demonstrate the quality of our probabilistic reconstruction and show that explicit ambiguity modeling is better-suited for this challenging problem.
1 code implementation • CVPR 2022 • Igor Santesteban, Miguel A. Otaduy, Dan Casas
We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies.
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 • 22 Jun 2021 • Jiayi Wang, Franziska Mueller, Florian Bernard, Suzanne Sorli, Oleksandr Sotnychenko, Neng Qian, Miguel A. Otaduy, Dan Casas, Christian Theobalt
Moreover, we demonstrate that our approach offers previously unseen two-hand tracking performance from RGB, and quantitatively and qualitatively outperforms existing RGB-based methods that were not explicitly designed for two-hand interactions.
no code implementations • 15 Jun 2021 • Franziska Mueller, Micah Davis, Florian Bernard, Oleksandr Sotnychenko, Mickeal Verschoor, Miguel A. Otaduy, Dan Casas, Christian Theobalt
We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands.
1 code implementation • CVPR 2021 • Igor Santesteban, Nils Thuerey, Miguel A. Otaduy, Dan Casas
We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions.
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 • 17 Mar 2019 • Igor Santesteban, Miguel A. Otaduy, Dan Casas
We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape.
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 • CVPR 2018 • Franziska Mueller, Florian Bernard, Oleksandr Sotnychenko, Dushyant Mehta, Srinath Sridhar, Dan Casas, Christian Theobalt
We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence.
1 code implementation • 3 May 2017 • Dushyant Mehta, Srinath Sridhar, Oleksandr Sotnychenko, Helge Rhodin, Mohammad Shafiei, Hans-Peter Seidel, Weipeng Xu, Dan Casas, Christian Theobalt
A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton.
Ranked #16 on
Pose Estimation
on Leeds Sports Poses
no code implementations • ICCV 2017 • Franziska Mueller, Dushyant Mehta, Oleksandr Sotnychenko, Srinath Sridhar, Dan Casas, Christian Theobalt
We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments.
no code implementations • 31 Dec 2016 • Helge Rhodin, Christian Richardt, Dan Casas, Eldar Insafutdinov, Mohammad Shafiei, Hans-Peter Seidel, Bernt Schiele, Christian Theobalt
Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center.
no code implementations • 29 Nov 2016 • Dushyant Mehta, Helge Rhodin, Dan Casas, Pascal Fua, Oleksandr Sotnychenko, Weipeng Xu, Christian Theobalt
We propose a CNN-based approach for 3D human body pose estimation from single RGB images that addresses the issue of limited generalizability of models trained solely on the starkly limited publicly available 3D pose data.
Ranked #17 on
Pose Estimation
on Leeds Sports Poses
no code implementations • 21 Oct 2016 • Nadia Robertini, Dan Casas, Helge Rhodin, Hans-Peter Seidel, Christian Theobalt
We propose a new model-based method to accurately reconstruct human performances captured outdoors in a multi-camera setup.
no code implementations • 16 Oct 2016 • Srinath Sridhar, Franziska Mueller, Michael Zollhöfer, Dan Casas, Antti Oulasvirta, Christian Theobalt
However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately.
no code implementations • 23 Sep 2016 • Helge Rhodin, Christian Richardt, Dan Casas, Eldar Insafutdinov, Mohammad Shafiei, Hans-Peter Seidel, Bernt Schiele, Christian Theobalt
We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual reality headset.
no code implementations • 28 Jul 2016 • Helge Rhodin, Nadia Robertini, Dan Casas, Christian Richardt, Hans-Peter Seidel, Christian Theobalt
Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy.