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06/08/2023
Background Prompting for Improved Object Depth
Estimating the depth of objects from a single image is a valuable task f...
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11/25/2022
SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow
Scene flow estimation is a long-standing problem in computer vision, whe...
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11/20/2022
DynIBaR: Neural Dynamic Image-Based Rendering
We address the problem of synthesizing novel views from a monocular vide...
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05/31/2022
D^2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video
Given a monocular video, segmenting and decoupling dynamic objects while...
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10/21/2021
Learning 3D Semantic Segmentation with only 2D Image Supervision
With the recent growth of urban mapping and autonomous driving efforts, ...
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08/10/2021
Differentiable Surface Rendering via Non-Differentiable Sampling
We present a method for differentiable rendering of 3D surfaces that sup...
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08/02/2021
Consistent Depth of Moving Objects in Video
We present a method to estimate depth of a dynamic scene, containing arb...
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05/14/2021
Omnimatte: Associating Objects and Their Effects in Video
Computer vision is increasingly effective at segmenting objects in image...
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05/06/2021
LASR: Learning Articulated Shape Reconstruction from a Monocular Video
Remarkable progress has been made in 3D reconstruction of rigid structur...
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09/16/2020
Layered Neural Rendering for Retiming People in Video
We present a method for retiming people in an ordinary, natural video—ma...
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12/12/2019
Deep Structured Implicit Functions
The goal of this project is to learn a 3D shape representation that enab...
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12/12/2019
Local Deep Implicit Functions for 3D Shape
The goal of this project is to learn a 3D shape representation that enab...
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06/19/2019
Unsupervised Learning of Object Structure and Dynamics from Videos
Extracting and predicting object structure and dynamics from videos with...
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04/25/2019
Learning the Depths of Moving People by Watching Frozen People
We present a method for predicting dense depth in scenarios where both a...
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04/12/2019
Learning Shape Templates with Structured Implicit Functions
Template 3D shapes are useful for many tasks in graphics and vision, inc...
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06/15/2018
Unsupervised Training for 3D Morphable Model Regression
We present a method for training a regression network from image pixels ...
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11/14/2017
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Style transfer usually refers to the task of applying color and texture ...
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01/17/2017
