Search Results for author:
Found 32 papers, 18 papers with code
Morig: Motion-aware rigging of character meshes from point clouds
We present MoRig, a method that automatically rigs character meshes driven by single-view point cloud streams capturing the motion of performing characters.
MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation
As a result, the learned 2D representations are view-invariant and geometrically consistent, leading to better generalization when trained on a limited number of labeled shapes compared to alternatives that utilize self-supervision in 2D or 3D alone.
Audio-driven Neural Gesture Reenactment with Video Motion Graphs
We present a method that reenacts a high-quality video with gestures matching a target speech audio.
APES: Articulated Part Extraction from Sprite Sheets
Rigged puppets are one of the most prevalent representations to create 2D character animations.
ANISE: Assembly-based Neural Implicit Surface rEconstruction
We demonstrate that, when performing reconstruction by decoding part representations into implicit functions, our method achieves state-of-the-art part-aware reconstruction results from both images and sparse point clouds.
ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions
We present ASSET, a neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map.
Projective Urban Texturing
Re-targeting such 2D datasets to 3D geometry is challenging because the underlying shape, size, and layout of the urban structures in the photos do not correspond to the ones in the target meshes.
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation
We present PriFit, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks.
BuildingNet: Learning to Label 3D Buildings
We introduce BuildingNet: (a) a large-scale dataset of 3D building models whose exteriors are consistently labeled, (b) a graph neural network that labels building meshes by analyzing spatial and structural relations of their geometric primitives.
Ranked #1 on
3D Building Mesh Labeling
on BuildingNet-Mesh
Neural Strokes: Stylized Line Drawing of 3D Shapes
We show that, in contrast to previous image-based methods, the use of a geometric representation of 3D shape and 2D strokes allows the model to transfer important aspects of shape and texture style while preserving contours.
Learning Part Boundaries from 3D Point Clouds
We present a method that detects boundaries of parts in 3D shapes represented as point clouds.
RigNet: Neural Rigging for Articulated Characters
We present RigNet, an end-to-end automated method for producing animation rigs from input character models.
MakeItTalk: Speaker-Aware Talking-Head Animation
We present a method that generates expressive talking heads from a single facial image with audio as the only input.
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years.
ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds
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 geometric primitives.
Neural Contours: Learning to Draw Lines from 3D Shapes
This paper introduces a method for learning to generate line drawings from 3D models.
Cross-Shape Attention for Part Segmentation of 3D Point Clouds
We present a method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation.
Neural Shape Parsers for Constructive Solid Geometry
We investigate two architectures for this task --- a vanilla encoder (CNN) - decoder (RNN) and another architecture that augments the encoder with an explicit memory module based on the program execution stack.
Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation
We present a framework for learning representations of 3D shapes that reflect the information present in this meta data and show that it leads to improved generalization for semantic segmentation tasks.
Predicting Animation Skeletons for 3D Articulated Models via Volumetric Nets
We present a learning method for predicting animation skeletons for input 3D models of articulated characters.
SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings.
Learning Material-Aware Local Descriptors for 3D Shapes
Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods.
Deep Part Induction from Articulated Object Pairs
In this paper, we explore how the observation of different articulation states provides evidence for part structure and motion of 3D objects.
VisemeNet: Audio-Driven Animator-Centric Speech Animation
We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio.
Graphics
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.
Ranked #21 on
Semantic Segmentation
on ScanNet
(test mIoU metric)
CSGNet: Neural Shape Parser for Constructive Solid Geometry
In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network.
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints.
Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks
We present a new local descriptor for 3D shapes, directly applicable to a wide range of shape analysis problems such as point correspondences, semantic segmentation, affordance prediction, and shape-to-scan matching.
3D Shape Segmentation with Projective Convolutional Networks
Our architecture combines image-based Fully Convolutional Networks (FCNs) and surface-based Conditional Random Fields (CRFs) to yield coherent segmentations of 3D shapes.
Multi-view Convolutional Neural Networks for 3D Shape Recognition
A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors?
Ranked #78 on
3D Point Cloud Classification
on ModelNet40
