activity
20222024
most citedMarkerless human pose estimation for biomedical applications: a survey

51 citations · 59 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV20241 cited

Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset

Andrea Avogaro, Andrea Toaiari, Federico Cunico +5

We introduce HARPER, a novel dataset for 3D body pose estimation and forecast in dyadic interactions between users and Spot, the quadruped robot manufactured by Boston Dynamics. Th…

cs.LG20234 cited

A Machine Learning-oriented Survey on Tiny Machine Learning

Luigi Capogrosso, Federico Cunico, Dong Seon Cheng +2

The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardwar…

cs.CV2023

Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language

Francesco Taioli, Federico Cunico, Federico Girella +3

We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map for Visual Navigation with natural language query prompts. The recently proposed RNR-Map employs a grid st…

cs.CV202351 cited

Markerless human pose estimation for biomedical applications: a survey

Andrea Avogaro, Federico Cunico, Bodo Rosenhahn +1

Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application. HPE is often preferred to traditional marker-ba…

cs.CV2023

OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation

Federico Cunico, Federico Girella, Andrea Avogaro +3

Continuous mid-air hand gesture recognition based on captured hand pose streams is fundamental for human-computer interaction, particularly in AR / VR. However, many of the methods…

cs.DC20231 cited

Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications

Luigi Capogrosso, Federico Cunico, Michele Lora +3

Many recent pattern recognition applications rely on complex distributed architectures in which sensing and computational nodes interact together through a communication network. D…