53 citations · 207 across the 11 of their papers we have counts for
6 papers · 1 filter
Learning Part Boundaries from 3D Point Clouds
Marios Loizou, Melinos Averkiou, Evangelos Kalogerakis
We present a method that detects boundaries of parts in 3D shapes represented as point clouds. Our method is based on a graph convolutional network architecture that outputs a prob…
RigNet: Neural Rigging for Articulated Characters
Zhan Xu, Yang Zhou, Evangelos Kalogerakis +2
We present RigNet, an end-to-end automated method for producing animation rigs from input character models. Given an input 3D model representing an articulated character, RigNet pr…
Neural Contours: Learning to Draw Lines from 3D Shapes
Difan Liu, Mohamed Nabail, Aaron Hertzmann +1
This paper introduces a method for learning to generate line drawings from 3D models. Our architecture incorporates a differentiable module operating on geometric features of the 3…
MakeItTalk: Speaker-Aware Talking-Head Animation
Yang Zhou, Xintong Han, Eli Shechtman +3
We present a method that generates expressive talking heads from a single facial image with audio as the only input. In contrast to previous approaches that attempt to learn direct…
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma +5
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. Both of these problems, however, suffer fr…
ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds
Gopal Sharma, Difan Liu, Subhransu Maji +3
We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic…