Search Results for author:
Found 8 papers, 2 papers with code
Data-Free Learning of Reduced-Order Kinematics
Physical systems ranging from elastic bodies to kinematic linkages are defined on high-dimensional configuration spaces, yet their typical low-energy configurations are concentrated on much lower-dimensional subspaces.
HPNet: Deep Primitive Segmentation Using Hybrid Representations
This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape represented as a point cloud into primitive patches.
Functional Generative Design: An Evolutionary Approach to 3D-Printing
The proposed methodology for discovering solutions to this problem consists of three components: First, an effective search space is learned through a variational autoencoder (VAE); second, a surrogate model for functional designs is built; and third, a genetic algorithm is used to simultaneously update the hyperparameters of the surrogate and to optimize the designs using the updated surrogate.
Dense Human Body Correspondences Using Convolutional Networks
We propose a deep learning approach for finding dense correspondences between 3D scans of people.
Capturing Dynamic Textured Surfaces of Moving Targets
We present an end-to-end system for reconstructing complete watertight and textured models of moving subjects such as clothed humans and animals, using only three or four handheld sensors.
Deep Generative Modeling for Scene Synthesis via Hybrid Representations
We show a principled way to train this model by combining discriminator losses for both a 3D object arrangement representation and a 2D image-based representation.
Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs
We introduce an approach for establishing dense correspondences between partial scans of human models and a complete template model.
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.
