Observable Subspaces for 3D Human Motion Recovery

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This paper presents a comprehensive framework for extracting georeferenced vehicle trajectories from high-altitude drone imagery, addressing key challenges in urban traffic monitoring and the limitations of traditional ground-based systems. Our approach in ...

We propose a test -time adaptation for 6D object pose tracking that learns to adapt a pre -trained model to track the 6D pose of novel objects. We consider the problem of 6D object pose tracking as a 3D keypoint detection and matching task and present a mo ...

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

VR (Virtual Reality) is a real-time simulation that creates the subjective illusion of being in a virtual world. This thesis explores how integrating the user's body and fingers can be achieved and beneficial for the user to experience VR. At the advent of ...

Purpose: To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-enco ...

Human motion analysis and synthesis is integral to many computer vision applications, from autonomous driving to sports analysis. In this thesis, we address several problems in this domain. First we consider active viewpoint selection for pose estimation w ...

Does gravity affect decision-making? This question comes into sharp focus as plans for interplanetary human space missions solidify. In the framework of Bayesian brain theories, gravity encapsulates a strong prior, anchoring agents to a reference frame via ...

Integration across space and time is essential for the analysis of motion, low contrast, and many more stimuli. A crucial question is what determines the duration of integration. Based on classical models of decision-making, one might expect that integrati ...

The thesis at hand is concerned with robots' navigation in human crowds. Specifically, methods are developed for planning a mobile robot's local motion between pedestrians, and they are evaluated in experiments where a robot interacts with real pedestrians ...

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 ...

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

Reduced-order models are indispensable for multi-query or real-time problems. However, there are still many challenges to constructing efficient ROMs for time-dependent parametrized problems. Using a linear reduced space is inefficient for time-dependent n ...

Detecting people from 2D images and analyzing their motion in 3D have been long standing computer vision problems central to numerous applications such as autonomous driving and athletic training. Recently, with the availability of large amounts of trainin ...

Being able to understand how optical forces emerge from the interaction of light with matter is paramount for controlling the motion of nanoparticles as well as powering nanomotors. The purpose of this work is to uncover the physical mechanisms at the orig ...

Picking up objects to toss them on a conveyor belt are activities generated on a daily basis in the industry. These tasks are still done largely by humans. This paper proposes a unified motion generator for a bimanual robotic systems that enables two 7 deg ...

MRI is a non-invasive medical imaging modality that is sensitive to patient motion, which constitutes a major limitation in most clinical applications. Solutions may arise from the reduction of acquisition times or from motion-correction techniques, either ...

Advances in soft sensors coupled with machine learning are enabling increasingly capable wearable systems. Since hand motion in particular can convey useful information for developing intuitive interfaces, glove-based systems can have a significant impact ...

In this paper we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich lat ...

The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental w ...

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

Observable Subspaces for 3D Human Motion Recovery

Chattez avec Graph Search

Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery

Test-time adaptation for 6D pose tracking

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

From finger animation to full-body embodiment of avatars with different morphologies and proportions

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI

Automated Human Motion Analysis and Synthesis

Microgravity induces overconfidence in perceptual decision-making

Processing load, and not stimulus evidence, determines the duration of unconscious visual feature integration

Geometric and Learning Methods for Robots to Navigate in Human Crowds with Application to Smart Mobility Devices

Diff3DHPE: A Diffusion Model for 3D Human Pose Estimation

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems

Encoder-Decoder Models for Human Segmentation and Motion Analysis

Explanation of Optical Force and Torque on Nanomotors as the Interplay of Huygens Sources

Dual-Arm Control for Coordinated Fast Grabbing and Tossing of an Object

Choreography Controlled (ChoCo) brain MRI artifact generation for labeled motion-corrupted datasets

A Wearable Smart Glove and Its Application of Pose and Gesture Detection to Sign Language Classification

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

Measuring and modeling the motor system with machine learning

Memory of Motion for Initializing Optimization in Robotics