348 citations · 1.4k across the 34 of their papers we have counts for
27 papers · 1 filter
DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation
Ying-Tian Liu, Zhifei Zhang, Yuan-Chen Guo +3
Automatic generation of fonts can be an important aid to typeface design. Many current approaches regard glyphs as pixelated images, which present artifacts when scaling and inevit…
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly
Xianghao Xu, Paul Guerrero, Matthew Fisher +2
Representing a 3D shape with a set of primitives can aid perception of structure, improve robotic object manipulation, and enable editing, stylization, and compression of 3D shapes…
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
Titas Anciukevičius, Zexiang Xu, Matthew Fisher +4
Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support task…
APES: Articulated Part Extraction from Sprite Sheets
Zhan Xu, Matthew Fisher, Yang Zhou +4
Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving pa…
Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
Tom Monnier, Matthew Fisher, Alexei A. Efros +1
Approaches for single-view reconstruction typically rely on viewpoint annotations, silhouettes, the absence of background, multiple views of the same instance, a template shape, or…
Neural Strokes: Stylized Line Drawing of 3D Shapes
Difan Liu, Matthew Fisher, Aaron Hertzmann +1
This paper introduces a model for producing stylized line drawings from 3D shapes. The model takes a 3D shape and a viewpoint as input, and outputs a drawing with textured strokes,…
Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition
James Hong, Matthew Fisher, Michaël Gharbi +1
Human pose is a useful feature for fine-grained sports action understanding. However, pose estimators are often unreliable when run on sports video due to domain shift and factors…
Font Completion and Manipulation by Cycling Between Multi-Modality Representations
Ye Yuan, Wuyang Chen, Zhaowen Wang +4
Generating font glyphs of consistent style from one or a few reference glyphs, i.e., font completion, is an important task in topographical design. As the problem is more well-defi…
GLASS: Geometric Latent Augmentation for Shape Spaces
Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman +3
We investigate the problem of training generative models on a very sparse collection of 3D models. We use geometrically motivated energies to augment and thus boost a sparse collec…
A Multi-Implicit Neural Representation for Fonts
Pradyumna Reddy, Zhifei Zhang, Matthew Fisher +3
Fonts are ubiquitous across documents and come in a variety of styles. They are either represented in a native vector format or rasterized to produce fixed resolution images. In th…
MarioNette: Self-Supervised Sprite Learning
Dmitriy Smirnov, Michael Gharbi, Matthew Fisher +3
Artists and video game designers often construct 2D animations using libraries of sprites -- textured patches of objects and characters. We propose a deep learning approach that de…
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing
Tobias Hinz, Matthew Fisher, Oliver Wang +2
We introduce CharacterGAN, a generative model that can be trained on only a few samples (8 - 15) of a given character. Our model generates novel poses based on keypoint locations,…
DECOR-GAN: 3D Shape Detailization by Conditional Refinement
Zhiqin Chen, Vladimir G. Kim, Matthew Fisher +3
We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. We address the challenge of creating large varieties…
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
Akshay Gadi Patil, Manyi Li, Matthew Fisher +2
We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout m…
COALESCE: Component Assembly by Learning to Synthesize Connections
Kangxue Yin, Zhiqin Chen, Siddhartha Chaudhuri +3
We introduce COALESCE, the first data-driven framework for component-based shape assembly which employs deep learning to synthesize part connections. To handle geometric and topolo…
Modeling Artistic Workflows for Image Generation and Editing
Hung-Yu Tseng, Matthew Fisher, Jingwan Lu +3
People often create art by following an artistic workflow involving multiple stages that inform the overall design. If an artist wishes to modify an earlier decision, significant w…
Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling
Omid Poursaeed, Matthew Fisher, Noam Aigerman +1
We propose a novel neural architecture for representing 3D surfaces, which harnesses two complementary shape representations: (i) an explicit representation via an atlas, i.e., emb…
Improved Techniques for Training Single-Image GANs
Tobias Hinz, Matthew Fisher, Oliver Wang +1
Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset. This task is of practical significance,…
UprightNet: Geometry-Aware Camera Orientation Estimation from Single Images
Wenqi Xian, Zhengqi Li, Matthew Fisher +3
We introduce UprightNet, a learning-based approach for estimating 2DoF camera orientation from a single RGB image of an indoor scene. Unlike recent methods that leverage deep learn…
Learning elementary structures for 3D shape generation and matching
Theo Deprelle, Thibault Groueix, Matthew Fisher +3
We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape. We…
Unsupervised cycle-consistent deformation for shape matching
Thibault Groueix, Matthew Fisher, Vladimir G. Kim +2
We propose a self-supervised approach to deep surface deformation. Given a pair of shapes, our algorithm directly predicts a parametric transformation from one shape to the other r…
Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction
Chen-Hsuan Lin, Oliver Wang, Bryan C. Russell +4
In this paper, we address the problem of 3D object mesh reconstruction from RGB videos. Our approach combines the best of multi-view geometric and data-driven methods for 3D recons…
BAE-NET: Branched Autoencoder for Shape Co-Segmentation
Zhiqin Chen, Kangxue Yin, Matthew Fisher +2
We treat shape co-segmentation as a representation learning problem and introduce BAE-NET, a branched autoencoder network, for the task. The unsupervised BAE-NET is trained with a…
3D-CODED : 3D Correspondences by Deep Deformation
Thibault Groueix, Matthew Fisher, Vladimir G. Kim +2
We present a new deep learning approach for matching deformable shapes by introducing {\it Shape Deformation Networks} which jointly encode 3D shapes and correspondences. This is a…