10 citations · 29 across the 14 of their papers we have counts for
15 papers
Reasoning in visual navigation of end-to-end trained agents: a dynamical systems approach
Steeven Janny, Hervé Poirier, Leonid Antsfeld +6
Progress in Embodied AI has made it possible for end-to-end-trained agents to navigate in photo-realistic environments with high-level reasoning and zero-shot or language-condition…
6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model
Matteo Bortolon, Theodore Tsesmelis, Stuart James +2
We propose 6DGS to estimate the camera pose of a target RGB image given a 3D Gaussian Splatting (3DGS) model representing the scene. 6DGS avoids the iterative process typical of an…
DCitizens Roles Unveiled: SIG Navigating Identities in Digital Civics and the Spectrum of Societal Impact
Anna R. L. Carter, Kyle Montague, Reem Talhouk +6
The DCitizens SIG aims to navigate ethical dimensions in forthcoming Digital Civics projects, ensuring enduring benefits and community resilience. Additionally, it seeks to shape t…
HAHA: Highly Articulated Gaussian Human Avatars with Textured Mesh Prior
David Svitov, Pietro Morerio, Lourdes Agapito +1
We present HAHA - a novel approach for animatable human avatar generation from monocular input videos. The proposed method relies on learning the trade-off between the use of Gauss…
IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single image and a NeRF model
Matteo Bortolon, Theodore Tsesmelis, Stuart James +2
We introduce IFFNeRF to estimate the six degrees-of-freedom (6DoF) camera pose of a given image, building on the Neural Radiance Fields (NeRF) formulation. IFFNeRF is specifically…
DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly
Gianluca Scarpellini, Stefano Fiorini, Francesco Giuliari +2
Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems. In this context, we posit that a general unified model…
mmFUSION: Multimodal Fusion for 3D Objects Detection
Javed Ahmad, Alessio Del Bue
Multi-sensor fusion is essential for accurate 3D object detection in self-driving systems. Camera and LiDAR are the most commonly used sensors, and usually, their fusion happens at…
Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation
Julio Ivan Davila Carrazco, Pietro Morerio, Alessio Del Bue +1
This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem. OS-UDA is the most chall…
Person Re-Identification without Identification via Event Anonymization
Shafiq Ahmad, Pietro Morerio, Alessio Del Bue
Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption (energy, bandwidth, and computation). Neuromorphic vis…
SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data
Mohammad Zohaib, Alessio Del Bue
This paper proposes a new method to infer keypoints from arbitrary object categories in practical scenarios where point cloud data (PCD) are noisy, down-sampled and arbitrarily rot…
Self-improving object detection via disagreement reconciliation
Gianluca Scarpellini, Stefano Rosa, Pietro Morerio +2
Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data. This paper studies how to automatical…
Look Around and Learn: Self-Training Object Detection by Exploration
Gianluca Scarpellini, Stefano Rosa, Pietro Morerio +2
When an object detector is deployed in a novel setting it often experiences a drop in performance. This paper studies how an embodied agent can automatically fine-tune a pre-existi…
VM-NeRF: Tackling Sparsity in NeRF with View Morphing
Matteo Bortolon, Alessio Del Bue, Fabio Poiesi
NeRF aims to learn a continuous neural scene representation by using a finite set of input images taken from various viewpoints. A well-known limitation of NeRF methods is their re…
Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints Invariance
Giancarlo Paoletti, Jacopo Cavazza, Cigdem Beyan +1
This paper presents a novel end-to-end method for the problem of skeleton-based unsupervised human action recognition. We propose a new architecture with a convolutional autoencode…
Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning
Giancarlo Paoletti, Jacopo Cavazza, Cigdem Beyan +1
This paper tackles the problem of human action recognition, defined as classifying which action is displayed in a trimmed sequence, from skeletal data. Albeit state-of-the-art appr…