Publication

Test-time adaptation for 6D pose tracking

Related publications (41)

Accurately estimating 3D human pose (3D HPE) and joint locations using only 2D keypoints is challenging. The noise in the predictions produced by conventional 2D human pose estimators often impeded the accuracy. In this paper, we present a diffusion-based ...

Los Alamitos2023

Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions

Mathieu Salzmann, Yinlin Hu, Shuxuan Guo

Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose estimation. In this work, we intro ...

Los Alamitos2023

Deformation-aware Unpaired Image Translation for Pose Estimation on Laboratory Animals

Pascal Fua, Pavan P Ramdya, Helge Jochen Rhodin, Semih Günel, Siyuan Li, Mirela Ostrek

Our goal is to capture the pose of neuroscience model organisms, without using any manual supervision, to be able to study how neural circuits orchestrate behaviour. Human pose estimation attains remarkable accuracy when trained on real or simulated datase ...

2020

ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture

Pascal Fua, Mathieu Salzmann, Helge Jochen Rhodin, Sena Kiciroglu, Sudipta Sinha

The accuracy of monocular 3D human pose estimation depends on the viewpoint from which the image is captured. While freely moving cameras, such as on drones, provide control over this viewpoint, automatically positioning them at the location which will yie ...

IEEE2020

Training Algorithms for Multiple Object Tracking

Andrii Maksai

Multiple object tracking is a crucial Computer Vision Task. It aims at locating objects of interest in the image sequences, maintaining their identities, and identifying their trajectories over time. A large portion of current research focuses on tracking ...

EPFL2019

Neural Scene Decomposition for Multi-Person Motion Capture

Pascal Fua, Mathieu Salzmann, Isinsu Katircioglu, Helge Jochen Rhodin, Victor Constantin

Learning general image representations has proven key to the success of many computer vision tasks. For example, many approaches to image understanding problems rely on deep networks that were initially trained on ImageNet, mostly because the learned featu ...

IEEE2019

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Bugra Tekin

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growin ...

EPFL2018

Real-Time Generative Hand Modeling and Tracking

Anastasia Tkach

In our everyday life we interact with the surrounding environment using our hands. A main focus of recent research has been to bring such interaction to virtual objects, such as the ones projected in virtual reality devices, or super-imposed as holograms i ...

EPFL2018

Markerless tracking of user-defined features with deep learning

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Pranav Mamidanna

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be h ...

2018

Physics-based Reconstruction and Animation of Humans

Alexandru Eugen Ichim

Creating digital representations of humans is of utmost importance for applications ranging from entertainment (video games, movies) to human-computer interaction and even psychiatrical treatments. What makes building credible digital doubles difficult is ...

EPFL2017

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