91 citations · 235 across the 20 of their papers we have counts for
29 papers
Zero-shot CAD Program Re-Parameterization for Interactive Manipulation
Milin Kodnongbua, Benjamin T. Jones, Maaz Bin Safeer Ahmad +2
Parametric CAD models encode entire families of shapes that should, in principle, be easy for designers to explore. However, in practice, parametric CAD models can be difficult to…
Self-Supervised Representation Learning for CAD
Benjamin T. Jones, Michael Hu, Vladimir G. Kim +1
The design of man-made objects is dominated by computer aided design (CAD) tools. Assisting design with data-driven machine learning methods is hampered by lack of labeled data in…
AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assemblies
Benjamin Jones, Dalton Hildreth, Duowen Chen +3
Assembly modeling is a core task of computer aided design (CAD), comprising around one third of the work in a CAD workflow. Optimizing this process therefore represents a huge oppo…
Joint Learning of 3D Shape Retrieval and Deformation
Mikaela Angelina Uy, Vladimir G. Kim, Minhyuk Sung +3
We propose a novel technique for producing high-quality 3D models that match a given target object image or scan. Our method is based on retrieving an existing shape from a databas…
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…
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…
Self-supervised Learning of Point Clouds via Orientation Estimation
Omid Poursaeed, Tianxing Jiang, Han Qiao +2
Point clouds provide a compact and efficient representation of 3D shapes. While deep neural networks have achieved impressive results on point cloud learning tasks, they require ma…
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…
Neural Subdivision
Hsueh-Ti Derek Liu, Vladimir G. Kim, Siddhartha Chaudhuri +2
This paper introduces Neural Subdivision, a novel framework for data-driven coarse-to-fine geometry modeling. During inference, our method takes a coarse triangle mesh as input and…
Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement
Sai Bi, Kalyan Sunkavalli, Federico Perazzi +3
We present a method to improve the visual realism of low-quality, synthetic images, e.g. OpenGL renderings. Training an unpaired synthetic-to-real translation network in image spac…
Affinity Graph Supervision for Visual Recognition
Chu Wang, Babak Samari, Vladimir G. Kim +2
Affinity graphs are widely used in deep architectures, including graph convolutional neural networks and attention networks. Thus far, the literature has focused on abstracting fea…
Neural Cages for Detail-Preserving 3D Deformations
Wang Yifan, Noam Aigerman, Vladimir G. Kim +2
We propose a novel learnable representation for detail-preserving shape deformation. The goal of our method is to warp a source shape to match the general structure of a target sha…
Neural Puppet: Generative Layered Cartoon Characters
Omid Poursaeed, Vladimir G. Kim, Eli Shechtman +2
We propose a learning based method for generating new animations of a cartoon character given a few example images. Our method is designed to learn from a traditionally animated se…
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…
Deep Parametric Shape Predictions using Distance Fields
Dmitriy Smirnov, Matthew Fisher, Vladimir G. Kim +2
Many tasks in graphics and vision demand machinery for converting shapes into consistent representations with sparse sets of parameters; these representations facilitate rendering,…
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…
Learning Material-Aware Local Descriptors for 3D Shapes
Hubert Lin, Melinos Averkiou, Evangelos Kalogerakis +5
Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolut…
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…
Learning Fuzzy Set Representations of Partial Shapes on Dual Embedding Spaces
Minhyuk Sung, Anastasia Dubrovina, Vladimir G. Kim +1
Modeling relations between components of 3D objects is essential for many geometry editing tasks. Existing techniques commonly rely on labeled components, which requires substantia…
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
Thibault Groueix, Matthew Fisher, Vladimir G. Kim +2
We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to metho…
Multi-Content GAN for Few-Shot Font Style Transfer
Samaneh Azadi, Matthew Fisher, Vladimir Kim +3
In this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typ…
SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images
Moos Hueting, Pradyumna Reddy, Vladimir Kim +3
Discovering 3D arrangements of objects from single indoor images is important given its many applications including interior design, content creation, etc. Although heavily researc…