Search Results for author:
Found 11 papers, 1 papers with code
Virtual Elastic Objects
We present Virtual Elastic Objects (VEOs): virtual objects that not only look like their real-world counterparts but also behave like them, even when subject to novel interactions.
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor
We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well.
DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models
Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images.
Capturing Detailed Deformations of Moving Human Bodies
The key idea behind our system is a new type of motion capture suit which contains a special pattern with checkerboard-like corners and two-letter codes.
Differentiable Implicit Soft-Body Physics
In contrast to other differentiable physics approaches that use explicit forward models to define state transitions, we focus on implicit state transitions defined via function minimization.
Unsupervised Shape Normality Metric for Severity Quantification
The severity of an anatomical deformity often serves as a determinant in the clinical management of patients.
Self-Supervised Discovery of Anatomical Shape Landmarks
In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis.
A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration
We propose a novel neural network architecture that simultaneously learns and uses the population-level statistics of the spatial transformations to regularize the neural networks for unsupervised image registration.
CoopSubNet: Cooperating Subnetwork for Data-Driven Regularization of Deep Networks under Limited Training Budgets
Deep networks are an integral part of the current machine learning paradigm.
DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images
Statistical shape modeling is an important tool to characterize variation in anatomical morphology.
Towards Real-time Simulation of Hyperelastic Materials
In this paper, we show that Projective Dynamics can be interpreted as a quasi-Newton method.
Graphics
