Search Results for author:
Found 37 papers, 20 papers with code
DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement
Given a coarse voxel shape (e. g., one produced with a simple box extrusion tool or via generative modeling), a user can directly "paint" desired target styles representing compelling geometric details, from input exemplar shapes, over different regions of the coarse shape.
Temporal Residual Jacobians For Rig-free Motion Transfer
We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer.
Learning to Infer Generative Template Programs for Visual Concepts
We develop a learning paradigm that allows us to train networks that infer Template Programs directly from visual datasets that contain concept groupings.
GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis
We introduce GEM3D -- a new deep, topology-aware generative model of 3D shapes.
Explorable Mesh Deformation Subspaces from Unstructured Generative Models
Deep generative models of 3D shapes often feature continuous latent spaces that can, in principle, be used to explore potential variations starting from a set of input shapes.
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly
We instead propose to decompose shapes using a library of 3D parts provided by the user, giving full control over the choice of parts.
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
Our key insight is to copy and deform patches from the partial input to complete missing regions.
Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art.
The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts
We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning.
ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives
Selection functionality is as fundamental to vector graphics as it is for raster data.
Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases
The key to making these correspondences semantically meaningful is to guarantee that the metric tensors computed at corresponding points are as similar as possible.
Ranked #1 on
Surface Reconstruction
on ANIM
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
GLASS: Geometric Latent Augmentation for Shape Spaces
We investigate the problem of training generative models on a very sparse collection of 3D models.
Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases
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 meaningful correspondences between all keyframes.
A Unified Batch Selection Policy for Active Metric Learning
To avoid redundancy between triplets, our method collectively selects batches with maximum joint entropy, which simultaneously captures both informativeness and diversity.
Joint Learning of 3D Shape Retrieval and Deformation
In fact, we use the embedding space to guide the shape pairs used to train the deformation module, so that it invests its capacity in learning deformations between meaningful shape pairs.
DECOR-GAN: 3D Shape Detailization by Conditional Refinement
During testing, a style code is fed into the generator to condition the refinement.
COALESCE: Component Assembly by Learning to Synthesize Connections
We introduce COALESCE, the first data-driven framework for component-based shape assembly which employs deep learning to synthesize part connections.
Batch Decorrelation for Active Metric Learning
We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$.
Neural Subdivision
During inference, our method takes a coarse triangle mesh as input and recursively subdivides it to a finer geometry by applying the fixed topological updates of Loop Subdivision, but predicting vertex positions using a neural network conditioned on the local geometry of a patch.
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.
Affinity Graph Supervision for Visual Recognition
Affinity graphs are widely used in deep architectures, including graph convolutional neural networks and attention networks.
Neural Cages for Detail-Preserving 3D Deformations
The goal of our method is to warp a source shape to match the general structure of a target shape, while preserving the surface details of the source.
FAN: Focused Attention Networks
Thus far the learning of attention weights has been driven solely by the minimization of task specific loss functions.
PerceptNet: Learning Perceptual Similarity of Haptic Textures in Presence of Unorderable Triplets
In order to design haptic icons or build a haptic vocabulary, we require a set of easily distinguishable haptic signals to avoid perceptual ambiguity, which in turn requires a way to accurately estimate the perceptual (dis)similarity of such signals.
BAE-NET: Branched Autoencoder for Shape Co-Segmentation
The unsupervised BAE-NET is trained with a collection of un-segmented shapes, using a shape reconstruction loss, without any ground-truth labels.
AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
While the part prior network can be trained with noisy and inconsistently segmented shapes, the final output of AdaCoSeg is a consistent part labeling for the input set, with each shape segmented into up to (a user-specified) K parts.
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.
SCORES: Shape Composition with Recursive Substructure Priors
The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss.
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently.
Graphics
Generalizing Across Domains via Cross-Gradient Training
We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains.
Ranked #92 on
Domain Generalization
on PACS
A Deep Recurrent Framework for Cleaning Motion Capture Data
We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data.
Tags2Parts: Discovering Semantic Regions from Shape Tags
We test our method on segmentation benchmarks and show that even with weak supervision of whole shape tags, our method can infer meaningful semantic regions, without ever observing shape segmentations.
ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling
The combinatorial nature of part arrangements poses another challenge, since the retrieval network is not a function: several complements can be appropriate for the same input.
Graphics I.3.5
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.
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures.
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.
