12 papers
Patch-based Representation and Learning for Efficient Deformation Modeling
Ruochen Chen, Thuy Tran, Shaifali Parashar
In this paper, we present a patch-based representation of surfaces, PolyFit, which is obtained by fitting jet functions locally on surface patches. Such a representation can be lea…
FNOPT: Resolution-Agnostic, Self-Supervised Cloth Simulation using Meta-Optimization with Fourier Neural Operators
Ruochen Chen, Thuy Tran, Shaifali Parashar
We present FNOpt, a self-supervised cloth simulation framework that formulates time integration as an optimization problem and trains a resolution-agnostic neural optimizer paramet…
Non-Rigid Structure-from-Motion via Differential Geometry with Recoverable Conformal Scale
Yongbo Chen, Yanhao Zhang, Shaifali Parashar +2
Non-rigid structure-from-motion (NRSfM), a promising technique for addressing the mapping challenges in monocular visual deformable simultaneous localization and mapping (SLAM), ha…
Image-Guided Shape-from-Template Using Mesh Inextensibility Constraints
Thuy Tran, Ruochen Chen, Shaifali Parashar
Shape-from-Template (SfT) refers to the class of methods that reconstruct the 3D shape of a deforming object from images/videos using a 3D template. Traditional SfT methods require…
GAPS: Geometry-Aware, Physics-Based, Self-Supervised Neural Garment Draping
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. Material-specific parameters are used…
Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases
Jan Bednarik, Noam Aigerman, Vladimir G. Kim +4
We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. It yields dense and semantically…
Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases
Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri +4
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 me…
A Closed-Form Solution to Local Non-Rigid Structure-from-Motion
Shaifali Parashar, Yuxuan Long, Mathieu Salzmann +1
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 o…
Robust Isometric Non-Rigid Structure-from-Motion
Shaifali Parashar, Adrien Bartoli, Daniel Pizarro
Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from the correspondences established between monocular 2D images. Current NRSfM methods lack statistical…
GarNet++: Improving Fast and Accurate Static3D Cloth Draping by Curvature Loss
Erhan Gundogdu, Victor Constantin, Shaifali Parashar +4
In this paper, we tackle the problem of static 3D cloth draping on virtual human bodies. We introduce a two-stream deep network model that produces a visually plausible draping of…
Shape Reconstruction by Learning Differentiable Surface Representations
Jan Bednarik, Shaifali Parashar, Erhan Gundogdu +2
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 represent…
DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequences
Jose Lamarca, Shaifali Parashar, Adrien Bartoli +1
Monocular SLAM algorithms perform robustly when observing rigid scenes, however, they fail when the observed scene deforms, for example, in medical endoscopy applications. We prese…