1 code implementation • 3 Dec 2023 • Ruochen Chen, Liming Chen, Shaifali Parashar
Recent neural, physics-based modeling of garment deformations allows faster and visually aesthetic results as opposed to the existing methods.
1 code implementation • 12 Nov 2021 • Jan Bednarik, Noam Aigerman, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua
The key to making these correspondences semantically meaningful is to guarantee that the metric tensors computed at corresponding points are as similar as possible.
Ranked #1 on Surface Reconstruction on ANIM
1 code implementation • ICCV 2021 • Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua, Noam Aigerman
We propose a method for the unsupervised reconstruction of a temporally-coherent sequence of surfaces from a sequence of time-evolving point clouds, yielding dense, semantically meaningful correspondences between all keyframes.
no code implementations • 23 Nov 2020 • Shaifali Parashar, Yuxuan Long, Mathieu Salzmann, Pascal Fua
A recent trend in Non-Rigid Structure-from-Motion (NRSfM) is to express local, differential constraints between pairs of images, from which the surface normal at any point can be obtained by solving a system of polynomial equations.
no code implementations • 9 Oct 2020 • Shaifali Parashar, Adrien Bartoli, Daniel Pizarro
Step 1 computes the optical flow from correspondences, step 2 reconstructs each 3D point's normal vector using multiple reference images and integrates them to form surfaces with the best reference and step 3 rejects the 3D points that break isometry in their local neighborhood.
no code implementations • 20 Jul 2020 • Erhan Gundogdu, Victor Constantin, Shaifali Parashar, Amrollah Seifoddini, Minh Dang, Mathieu Salzmann, Pascal Fua
We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the body and garment shapes.
1 code implementation • CVPR 2020 • Shaifali Parashar, Mathieu Salzmann, Pascal Fua
We propose a new formulation to non-rigid structure-from-motion that only requires the deforming surface to preserve its differential structure.
1 code implementation • CVPR 2020 • Jan Bednarik, Shaifali Parashar, Erhan Gundogdu, Mathieu Salzmann, Pascal Fua
Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations.
1 code implementation • 20 Aug 2019 • Jose Lamarca, Shaifali Parashar, Adrien Bartoli, J. M. M. Montiel
In our experiments, DefSLAM processes close-up sequences of deforming scenes, both in a laboratory controlled experiment and in medical endoscopy sequences, producing accurate 3D models of the scene with respect to the moving camera.
no code implementations • ECCV 2018 • Shaifali Parashar, Adrien Bartoli, Daniel Pizarro
We present self-calibrating isometric non-rigid structure- from-motion (SCIso-NRSfM), the first method to reconstruct a non-rigid object from at least three monocular images with constant but unknown focal length.
no code implementations • CVPR 2016 • Shaifali Parashar, Daniel Pizarro, Adrien Bartoli
We study Isometric Non-Rigid Shape-from-Motion (Iso-NRSfM): given multiple intrinsically calibrated monocular images, we want to reconstruct the time-varying 3D shape of an object undergoing isometric deformations.
no code implementations • ICCV 2015 • Shaifali Parashar, Daniel Pizarro, Adrien Bartoli, Toby Collins
Volumetric SfT uses the object's full volume to express the deformation constraints and reconstructs the object's surface and interior deformation.