Search Results for author: Federica Bogo

Found 17 papers, 5 papers with code

FLAG: Flow-based 3D Avatar Generation from Sparse Observations

no code implementations CVPR 2022 Sadegh Aliakbarian, Pashmina Cameron, Federica Bogo, Andrew Fitzgibbon, Thomas J. Cashman

To represent people in mixed reality applications for collaboration and communication, we need to generate realistic and faithful avatar poses.

Mixed Reality

Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together

no code implementations3 Feb 2022 Jeffrey Delmerico, Roi Poranne, Federica Bogo, Helen Oleynikova, Eric Vollenweider, Stelian Coros, Juan Nieto, Marc Pollefeys

Spatial computing -- the ability of devices to be aware of their surroundings and to represent this digitally -- offers novel capabilities in human-robot interaction.

Mixed Reality

EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices

1 code implementation14 Dec 2021 Siwei Zhang, Qianli Ma, Yan Zhang, Zhiyin Qian, Taein Kwon, Marc Pollefeys, Federica Bogo, Siyu Tang

Key to reasoning about interactions is to understand the body pose and motion of the interaction partner from the egocentric view.

Motion Estimation

Learning to Fit Morphable Models

no code implementations29 Nov 2021 Vasileios Choutas, Federica Bogo, Jingjing Shen, Julien Valentin

A common first step in systems that tackle these problems is to regress the parameters of the parametric model directly from the input data.

Learning Motion Priors for 4D Human Body Capture in 3D Scenes

no code implementations ICCV 2021 Siwei Zhang, Yan Zhang, Federica Bogo, Marc Pollefeys, Siyu Tang

To prove the effectiveness of the proposed motion priors, we combine them into a novel pipeline for 4D human body capture in 3D scenes.

HoloLens 2 Research Mode as a Tool for Computer Vision Research

1 code implementation25 Aug 2020 Dorin Ungureanu, Federica Bogo, Silvano Galliani, Pooja Sama, Xin Duan, Casey Meekhof, Jan Stühmer, Thomas J. Cashman, Bugra Tekin, Johannes L. Schönberger, Pawel Olszta, Marc Pollefeys

Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.

Mixed Reality

The Phong Surface: Efficient 3D Model Fitting using Lifted Optimization

no code implementations ECCV 2020 Jingjing Shen, Thomas J. Cashman, Qi Ye, Tim Hutton, Toby Sharp, Federica Bogo, Andrew William Fitzgibbon, Jamie Shotton

Realtime perceptual and interaction capabilities in mixed reality require a range of 3D tracking problems to be solved at low latency on resource-constrained hardware such as head-mounted devices.

Mixed Reality

H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions

1 code implementation CVPR 2019 Bugra Tekin, Federica Bogo, Marc Pollefeys

Given a single RGB image, our model jointly estimates the 3D hand and object poses, models their interactions, and recognizes the object and action classes with a single feed-forward pass through a neural network.

Towards Accurate Markerless Human Shape and Pose Estimation over Time

no code implementations24 Jul 2017 Yinghao Huang, Federica Bogo, Christoph Lassner, Angjoo Kanazawa, Peter V. Gehler, Ijaz Akhter, Michael J. Black

Existing marker-less motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, which narrows its application scenarios.

Pose Estimation

Dynamic FAUST: Registering Human Bodies in Motion

no code implementations CVPR 2017 Federica Bogo, Javier Romero, Gerard Pons-Moll, Michael J. Black

We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology.

Unite the People: Closing the Loop Between 3D and 2D Human Representations

2 code implementations CVPR 2017 Christoph Lassner, Javier Romero, Martin Kiefel, Federica Bogo, Michael J. Black, Peter V. Gehler

With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body.

 Ranked #1 on Monocular 3D Human Pose Estimation on Human3.6M (Use Video Sequence metric)

3D human pose and shape estimation Monocular 3D Human Pose Estimation

Detailed Full-Body Reconstructions of Moving People From Monocular RGB-D Sequences

no code implementations ICCV 2015 Federica Bogo, Michael J. Black, Matthew Loper, Javier Romero

The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation.

FAUST: Dataset and Evaluation for 3D Mesh Registration

no code implementations CVPR 2014 Federica Bogo, Javier Romero, Matthew Loper, Michael J. Black

We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments.

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