no code implementations • 16 Jan 2024 • Siwei Zhang, Bharat Lal Bhatnagar, Yuanlu Xu, Alexander Winkler, Petr Kadlecek, Siyu Tang, Federica Bogo
We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling.
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.
no code implementations • 3 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.
1 code implementation • 14 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.
no code implementations • 29 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.
1 code implementation • 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.
no code implementations • ICCV 2021 • Taein Kwon, Bugra Tekin, Jan Stuhmer, Federica Bogo, Marc Pollefeys
To this end, we propose a method to create a unified dataset for egocentric 3D interaction recognition.
1 code implementation • 25 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.
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.
no code implementations • CVPR 2020 • Yana Hasson, Bugra Tekin, Federica Bogo, Ivan Laptev, Marc Pollefeys, Cordelia Schmid
Modeling hand-object manipulations is essential for understanding how humans interact with their environment.
Ranked #9 on hand-object pose on HO-3D
no code implementations • 22 Jul 2019 • Huseyin Coskun, Zeeshan Zia, Bugra Tekin, Federica Bogo, Nassir Navab, Federico Tombari, Harpreet Sawhney
The lack of large-scale real datasets with annotations makes transfer learning a necessity for video activity understanding.
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.
no code implementations • 24 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.
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.
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
2 code implementations • 27 Jul 2016 • Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, Michael J. Black
We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints.
Ranked #29 on 3D Human Pose Estimation on HumanEva-I
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.
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.