Search Results for author: Shaifali Parashar

Found 12 papers, 6 papers with code

GAPS: Geometry-Aware, Physics-Based, Self-Supervised Neural Garment Draping

1 code implementation3 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.

Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases

1 code implementation12 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.

Surface Reconstruction

Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases

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.

Surface Reconstruction

A Closed-Form Solution to Local Non-Rigid Structure-from-Motion

no code implementations23 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.

Robust Isometric Non-Rigid Structure-from-Motion

no code implementations9 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.

Optical Flow Estimation

GarNet++: Improving Fast and Accurate Static3D Cloth Draping by Curvature Loss

no code implementations20 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.

Local Non-Rigid Structure-From-Motion From Diffeomorphic Mappings

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.

Shape Reconstruction by Learning Differentiable Surface Representations

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.

DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequences

1 code implementation20 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.

Self-Calibrating Isometric Non-Rigid Structure-from-Motion

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.

Isometric Non-Rigid Shape-From-Motion in Linear Time

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.

As-Rigid-As-Possible Volumetric Shape-From-Template

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.

Object

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