91 citations · 235 across the 20 of their papers we have counts for
6 papers · 1 filter
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…
ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling
Minhyuk Sung, Hao Su, Vladimir G. Kim +2
Assembly-based tools provide a powerful modeling paradigm for non-expert shape designers. However, choosing a component from a large shape repository and aligning it to a partial a…
Tags2Parts: Discovering Semantic Regions from Shape Tags
Sanjeev Muralikrishnan, Vladimir G. Kim, Siddhartha Chaudhuri
We propose a novel method for discovering shape regions that strongly correlate with user-prescribed tags. For example, given a collection of chairs tagged as either "has armrest"…
Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks
Haibin Huang, Evangelos Kalogerakis, Siddhartha Chaudhuri +3
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 pre…
Learning Hierarchical Shape Segmentation and Labeling from Online Repositories
Li Yi, Leonidas Guibas, Aaron Hertzmann +3
We propose a method for converting geometric shapes into hierarchically segmented parts with part labels. Our key idea is to train category-specific models from the scene graphs an…