b'Kwang Moo Yi'

research

∙ 07/15/2023

INVE: Interactive Neural Video Editing

We present Interactive Neural Video Editing (INVE), a real-time video ed...

0 Jiahui Huang, et al. ∙

research

∙ 05/24/2023

Unsupervised Semantic Correspondence Using Stable Diffusion

Text-to-image diffusion models are now capable of generating images that...

0 Eric Hedlin, et al. ∙

research

∙ 05/18/2023

PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems

We develop a tool, which we name Protoplanetary Disk Operator Network (P...

0 Shunyuan Mao, et al. ∙

research

∙ 05/12/2023

BlendFields: Few-Shot Example-Driven Facial Modeling

Generating faithful visualizations of human faces requires capturing bot...

0 Kacper Kania, et al. ∙

research

∙ 04/24/2023

Pointersect: Neural Rendering with Cloud-Ray Intersection

We propose a novel method that renders point clouds as if they are surfa...

0 Jen-Hao Rick Chang, et al. ∙

research

∙ 03/29/2023

CN-DHF: Compact Neural Double Height-Field Representations of 3D Shapes

We introduce CN-DHF (Compact Neural Double-Height-Field), a novel hybrid...

0 Eric Hedlin, et al. ∙

research

∙ 03/27/2023

FaceLit: Neural 3D Relightable Faces

We propose a generative framework, FaceLit, capable of generating a 3D f...

0 Anurag Ranjan, et al. ∙

research

∙ 12/04/2022

Neural Fourier Filter Bank

We present a novel method to provide efficient and highly detailed recon...

0 Zhijie Wu, et al. ∙

research

∙ 10/27/2022

Bootstrapping Human Optical Flow and Pose

We propose a bootstrapping framework to enhance human optical flow and p...

0 Aritro Roy Arko, et al. ∙

research

∙ 09/21/2022

Attention Beats Concatenation for Conditioning Neural Fields

Neural fields model signals by mapping coordinate inputs to sampled valu...

53 Daniel Rebain, et al. ∙

research

∙ 08/03/2022

Estimating Visual Information From Audio Through Manifold Learning

We propose a new framework for extracting visual information about a sce...

1 Fabrizio Pedersoli, et al. ∙

research

∙ 07/20/2022

NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds

We introduce a method for instance proposal generation for 3D point clou...

9 Weiwei Sun, et al. ∙

research

∙ 06/16/2022

TUSK: Task-Agnostic Unsupervised Keypoints

Existing unsupervised methods for keypoint learning rely heavily on the ...

0 Yuhe Jin, et al. ∙

research

∙ 05/27/2022

FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging

To correct for breathing motion in PET imaging, an interpretable and uns...

0 Teaghan O'Briain, et al. ∙

research

∙ 04/29/2022

A Simple Method to Boost Human Pose Estimation Accuracy by Correcting the Joint Regressor for the Human3.6m Dataset

Many human pose estimation methods estimate Skinned Multi-Person Linear ...

2 Eric Hedlin, et al. ∙

research

∙ 03/23/2022

NeuMan: Neural Human Radiance Field from a Single Video

Photorealistic rendering and reposing of humans is important for enablin...

2 Wei Jiang, et al. ∙

research

∙ 01/07/2022

Repurposing Existing Deep Networks for Caption and Aesthetic-Guided Image Cropping

We propose a novel optimization framework that crops a given image based...

11 Nora Horanyi, et al. ∙

research

∙ 12/03/2021

CoNeRF: Controllable Neural Radiance Fields

We extend neural 3D representations to allow for intuitive and interpret...

0 Kacper Kania, et al. ∙

research

∙ 11/24/2021

Layered Controllable Video Generation

We introduce layered controllable video generation, where we, without an...

0 Jiahui Huang, et al. ∙

research

∙ 11/19/2021

LOLNeRF: Learn from One Look

We present a method for learning a generative 3D model based on neural r...

23 Daniel Rebain, et al. ∙

research

∙ 06/07/2021

Deep Medial Fields

Implicit representations of geometry, such as occupancy fields or signed...

0 Daniel Rebain, et al. ∙

research

∙ 03/25/2021

COTR: Correspondence Transformer for Matching Across Images

We propose a novel framework for finding correspondences in images based...

0 Wei Jiang, et al. ∙

research

∙ 12/08/2020

Canonical Capsules: Unsupervised Capsules in Canonical Pose

We propose an unsupervised capsule architecture for 3D point clouds. We ...

6 Weiwei Sun, et al. ∙

research

∙ 11/25/2020

DeRF: Decomposed Radiance Fields

With the advent of Neural Radiance Fields (NeRF), neural networks can no...

8 Daniel Rebain, et al. ∙

research

∙ 04/15/2020

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Many classical Computer Vision problems, such as essential matrix comput...

0 Zheng Dang, et al. ∙

research

∙ 03/25/2020

VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning

Active Learning for discriminative models has largely been studied with ...

0 Jongwon Choi, et al. ∙

research

∙ 03/03/2020

Image Matching across Wide Baselines: From Paper to Practice

We introduce a comprehensive benchmark for local features and robust est...

36 Yuhe Jin, et al. ∙

research

∙ 12/08/2019

VoronoiNet: General Functional Approximators with Local Support

Voronoi diagrams are highly compact representations that are used in var...

0 Francis Williams, et al. ∙

research

∙ 11/25/2019

Reducing the Human Effort in Developing PET-CT Registration

We aim to reduce the tedious nature of developing and evaluating methods...

0 Teaghan O'Briain, et al. ∙

research

∙ 09/17/2019

Optimizing Through Learned Errors for Accurate Sports Field Registration

We propose an optimization-based framework to register sports field temp...

13 Wei Jiang, et al. ∙

research

∙ 08/15/2019

Beyond Cartesian Representations for Local Descriptors

The dominant approach for learning local patch descriptors relies on sma...

2 Patrick Ebel, et al. ∙

research

∙ 07/04/2019

Attentive Context Normalization for Robust Permutation-Equivariant Learning

Many problems in computer vision require dealing with sparse, unstructur...

0 Weiwei Sun, et al. ∙

research

∙ 01/22/2019

Linearized Multi-Sampling for Differentiable Image Transformation

We propose a novel image sampling method for differentiable image transf...

0 Wei Jiang, et al. ∙

research

∙ 01/14/2019

Self-Supervised Deep Active Accelerated MRI

We propose to simultaneously learn to sample and reconstruct magnetic re...

14 Kyong Hwan Jin, et al. ∙

research

∙ 11/26/2018

MIST: Multiple Instance Spatial Transformer Network

We propose a deep network that can be trained to tackle image reconstruc...

0 Baptiste Angles, et al. ∙

research

∙ 05/24/2018

LF-Net: Learning Local Features from Images

We present a novel deep architecture and a training strategy to learn a ...

0 Yuki Ono, et al. ∙

research

∙ 03/21/2018

Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses

Many classical Computer Vision problems, such as essential matrix comput...

0 Zheng Dang, et al. ∙

research

∙ 11/16/2017

Learning to Find Good Correspondences

We develop a deep architecture to learn to find good correspondences for...

0 Kwang Moo Yi, et al. ∙

research

∙ 09/05/2017

Authoring and Living Next-Generation Location-Based Experiences

Authoring location-based experiences involving multiple participants, co...

0 Olivier Balet, et al. ∙

research

∙ 03/30/2016

LIFT: Learned Invariant Feature Transform

We introduce a novel Deep Network architecture that implements the full ...

0 Kwang Moo Yi, et al. ∙

research

∙ 11/13/2015

Learning to Assign Orientations to Feature Points

We show how to train a Convolutional Neural Network to assign a canonica...

0 Kwang Moo Yi, et al. ∙

research

∙ 11/17/2014

TILDE: A Temporally Invariant Learned DEtector

We introduce a learning-based approach to detect repeatable keypoints un...

0 Yannick Verdie, et al. ∙

Kwang Moo Yi

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