3 citations · 3 across the 1 of their papers we have counts for
7 papers
Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation
Davis Wertheimer, Omid Poursaeed, Bharath Hariharan
We aim to build image generation models that generalize to new domains from few examples. To this end, we first investigate the generalization properties of classic image generator…
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…
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…
Fine-grained Synthesis of Unrestricted Adversarial Examples
Omid Poursaeed, Tianxing Jiang, Yordanos Goshu +3
We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation. Unlike existing unrestricted attacks that typ…
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…
Deep Fundamental Matrix Estimation without Correspondences
Omid Poursaeed, Guandao Yang, Aditya Prakash +4
Estimating fundamental matrices is a classic problem in computer vision. Traditional methods rely heavily on the correctness of estimated key-point correspondences, which can be no…