2 code implementations • 29 Apr 2022 • Eric Hedlin, Helge Rhodin, Kwang Moo Yi
While the quality of this pseudo-ground-truth is challenging to assess due to the lack of actual ground-truth SMPL, with the Human 3. 6m dataset, we qualitatively show that our joint locations are more accurate and that our regressor leads to improved pose estimations results on the test set without any need for retraining.
no code implementations • 23 Mar 2022 • Wei Jiang, Kwang Moo Yi, Golnoosh Samei, Oncel Tuzel, Anurag Ranjan
Photorealistic rendering and reposing of humans is important for enabling augmented reality experiences.
1 code implementation • 7 Mar 2022 • Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti, Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
no code implementations • 7 Jan 2022 • Nora Horanyi, Kedi Xia, Kwang Moo Yi, Abhishake Kumar Bojja, Ales Leonardis, Hyung Jin Chang
We propose a novel optimization framework that crops a given image based on user description and aesthetics.
no code implementations • 3 Dec 2021 • Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciński, Andrea Tagliasacchi
We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i. e. camera control).
no code implementations • 24 Nov 2021 • Jiahui Huang, Yuhe Jin, Kwang Moo Yi, Leonid Sigal
In the first stage, with the rich set of losses and dynamic foreground size prior, we learn how to separate the frame into foreground and background layers and, conditioned on these layers, how to generate the next frame using VQ-VAE generator.
no code implementations • 19 Nov 2021 • Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, Andrea Tagliasacchi
We present a method for learning a generative 3D model based on neural radiance fields, trained solely from data with only single views of each object.
1 code implementation • CVPR 2021 • Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.
no code implementations • 7 Jun 2021 • Daniel Rebain, Ke Li, Vincent Sitzmann, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi
Implicit representations of geometry, such as occupancy fields or signed distance fields (SDF), have recently re-gained popularity in encoding 3D solid shape in a functional form.
1 code implementation • ICCV 2021 • Wei Jiang, Eduard Trulls, Jan Hosang, Andrea Tagliasacchi, Kwang Moo Yi
We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other.
Ranked #1 on
Dense Pixel Correspondence Estimation
on ETH3D
Dense Pixel Correspondence Estimation
Optical Flow Estimation
1 code implementation • NeurIPS 2021 • Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey Hinton, Kwang Moo Yi
We propose a self-supervised capsule architecture for 3D point clouds.
no code implementations • CVPR 2021 • Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi
Moreover, we show that a Voronoi spatial decomposition is preferable for this purpose, as it is provably compatible with the Painter's Algorithm for efficient and GPU-friendly rendering.
no code implementations • 15 Apr 2020 • Zheng Dang, Kwang Moo Yi, Yinlin Hu, Fei Wang, Pascal Fua, Mathieu Salzmann
In this paper, we introduce an eigendecomposition-free approach to training a deep network whose loss depends on the eigenvector corresponding to a zero eigenvalue of a matrix predicted by the network.
no code implementations • CVPR 2021 • Jongwon Choi, Kwang Moo Yi, Ji-Hoon Kim, Jinho Choo, Byoungjip Kim, Jin-Yeop Chang, Youngjune Gwon, Hyung Jin Chang
We show that our method can be applied to classification tasks on multiple different datasets -- including one that is a real-world dataset with heavy data imbalance -- significantly outperforming the state of the art.
5 code implementations • 3 Mar 2020 • Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, Eduard Trulls
We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric.
no code implementations • 8 Dec 2019 • Francis Williams, Daniele Panozzo, Kwang Moo Yi, Andrea Tagliasacchi
Voronoi diagrams are highly compact representations that are used in various Graphics applications.
no code implementations • 25 Nov 2019 • Teaghan O'Briain, Kyong Hwan Jin, Hongyoon Choi, Erika Chin, Magdalena Bazalova-Carter, Kwang Moo Yi
We aim to reduce the tedious nature of developing and evaluating methods for aligning PET-CT scans from multiple patient visits.
no code implementations • 25 Sep 2019 • Baptiste Angles, Simon Kornblith, Shahram Izadi, Andrea Tagliasacchi, Kwang Moo Yi
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.
1 code implementation • 17 Sep 2019 • Wei Jiang, Juan Camilo Gamboa Higuera, Baptiste Angles, Weiwei Sun, Mehrsan Javan, Kwang Moo Yi
We propose an optimization-based framework to register sports field templates onto broadcast videos.
1 code implementation • ICCV 2019 • Patrick Ebel, Anastasiia Mishchuk, Kwang Moo Yi, Pascal Fua, Eduard Trulls
We demonstrate that this representation is particularly amenable to learning descriptors with deep networks.
1 code implementation • CVPR 2020 • Weiwei Sun, Wei Jiang, Eduard Trulls, Andrea Tagliasacchi, Kwang Moo Yi
Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds.
1 code implementation • ICCV 2019 • Wei Jiang, Weiwei Sun, Andrea Tagliasacchi, Eduard Trulls, Kwang Moo Yi
We propose a novel image sampling method for differentiable image transformation in deep neural networks.
no code implementations • 14 Jan 2019 • Kyong Hwan Jin, Michael Unser, Kwang Moo Yi
The reconstruction network is trained to give the highest reconstruction quality, given the MCTS sampling pattern.
1 code implementation • 26 Nov 2018 • Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.
Anomaly Detection In Surveillance Videos
Image Reconstruction
3 code implementations • NeurIPS 2018 • Yuki Ono, Eduard Trulls, Pascal Fua, Kwang Moo Yi
We present a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision.
no code implementations • ECCV 2018 • Zheng Dang, Kwang Moo Yi, Yinlin Hu, Fei Wang, Pascal Fua, Mathieu Salzmann
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be solved by finding the eigenvector corresponding to the smallest, or zero, eigenvalue of a matrix representing a linear system.
no code implementations • 16 Nov 2017 • Abhishake Kumar Bojja, Franziska Mueller, Sri Raghu Malireddi, Markus Oberweger, Vincent Lepetit, Christian Theobalt, Kwang Moo Yi, Andrea Tagliasacchi
We propose an automatic method for generating high-quality annotations for depth-based hand segmentation, and introduce a large-scale hand segmentation dataset.
3 code implementations • CVPR 2018 • Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, Pascal Fua
We develop a deep architecture to learn to find good correspondences for wide-baseline stereo.
1 code implementation • 30 Mar 2016 • Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, Pascal Fua
We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description.
no code implementations • ICCV 2015 • Alberto Crivellaro, Mahdi Rad, Yannick Verdie, Kwang Moo Yi, Pascal Fua, Vincent Lepetit
We present a method that estimates in real-time and under challenging conditions the 3D pose of a known object.
no code implementations • CVPR 2016 • Kwang Moo Yi, Yannick Verdie, Pascal Fua, Vincent Lepetit
We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point.
no code implementations • CVPR 2015 • Yannick Verdie, Kwang Moo Yi, Pascal Fua, Vincent Lepetit
We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive.